quantitative research about mental health of students

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Examining the mental health of university students: A quantitative and qualitative approach to identifying prevalence, associations, stressors, and interventions

Affiliations.

1 Department of Dental Public Health and Behavioural Sciences, University of Missouri-Kansas City School of Dentistry, Kansas City, MO, USA.
2 Office of Research and Graduate Programs, University of Missouri-Kansas City School of Dentistry, Kansas City, MO, USA.
PMID: 35380931
DOI: 10.1080/07448481.2022.2057192

Objective To identify the prevalence of anxiety, depression, and suicidal ideation that would place university students at risk for mental health disorders. To explore the source of stressors and possible interventions that may benefit student mental health in a university setting.

Participants: University students (n = 483) who had been learning remotely due to the COVID-19 pandemic.

Methods: A mixed-methods cross-sectional survey was administered in 2020.

Results: Students were at an increased rate of depression, anxiety and suicidal ideation as compared to the general population. Female gender, lack of social support, living alone, being a first-generation college student and COVID-19 were significantly associated with mental health disorders. Stressors were identified and categorized into themes and interventions were recognized that may improve student well-being.

Conclusion: Students enrolled in university programs appear to experience significant amounts of anxiety, depression, and suicidal ideation. Additional mental health education, resources, and support is needed.

Keywords: Anxiety; COVID-19; college students; depression; suicidal ideation.

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Published: 25 April 2023

Relationship between depression and quality of life among students: a systematic review and meta-analysis

Michele da Silva Valadão Fernandes 1 , 7 ,
Carolina Rodrigues Mendonça 2 ,
Thays Martins Vital da Silva 3 ,
Priscilla Rayanne e Silva Noll 1 , 4 ,
Luiz Carlos de Abreu 5 &
Matias Noll 1 , 6

Scientific Reports volume 13 , Article number: 6715 ( 2023 ) Cite this article

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Risk factors
Signs and symptoms

The objectives of this systematic review were to estimate the prevalence of depression and to identify the relationship between depression and quality of life (QOL) among high school and university students. Literature search was performed in the Scopus, Embase, PubMed, Scielo, CINAHL and Web of Science databases, following the PRISMA methodology. The results were presented through descriptive approaches and meta-analysis. Thirty-six studies met the eligibility criteria, and twenty-six were included in the meta-analysis. The prevalence of depressive symptoms was 27% (95% CI 0.21–0.33) among students, being high school and university students was 25% (95% CI 0.14–0.37) and 27% (95% CI 0.20–0.34), respectively, and most studies have shown that depression was associated with low QOL. Among the limitations of the study is the difficulty of generalizing the results found, considering the large sample of health students. New studies should be conducted considering the severity, duration, and patterns of depressive symptoms in high school and university students, to better understand the relationship between depression and QOL.

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Introduction.

Depression is a disorder that increasingly affects different populations, with an estimated prevalence rate of 4.4% worldwide 1 . This condition is defined as a mental disorder characterized by a persistent state of depressed mood, accompanied by other psychiatric symptoms such as fatigue and loss of energy, decreased interest or pleasure, impaired sleep, psychomotor agitation or retardation, concentration difficulties, change in appetite and weight, feelings of worthlessness or excessive guilt, or suicidal ideations 2 , 3 . Biological, psychological, cultural, and social factors can contribute to the risk of depression at some stage of life 4 , 5 , 6 , 7 . The high prevalence of depressive symptoms among high school and university students is a worrying aspect from the point of view of public health and educational policies 8 , 9 , 10 , 11 , 12 , because it interferes negatively with learning, performance, and academic success 13 , 14 , in addition to increasing the global burden of diseases 3 , 15 .

High school and university students present significant risk factors for depression, since they need to deal with academic stress on a daily basis 16 , 17 , 18 , 19 . This population is extremely concerned about school performance; emotional, family, and social conflicts; anxiety; among other aspects of life, common to adolescents and young adults, who need to adapt to changes in puberty 18 , 20 , 21 , 22 . On the other hand, interaction with a supportive environment in the educational context can contribute to the prevention and remission of depressive symptoms, improving the QOL among students 23 , 24 . Although different studies have shown that depression negatively impacts the QOL 25 , 26 , 27 , 28 , the relationship between the severity of depressive symptoms and QOL among high school and university students is unclear 21 , 29 .

Recent literature reviews have reported on the prevalence of depression in adolescents and their relationship with distinct biopsychosocial variables 4 , 22 , 30 , such as academic stress, sociodemographic correlates 12 , 31 , resilience 32 , school frequency 33 , and the school psychosocial climate 34 . Other reviews, with samples of university students, also prioritized the results of depression prevalence 35 , 36 and a wide variety of associated risk factors, such as sleep quality 37 , suicidal ideation 36 , 38 , sex 10 , 36 , 39 , socioeconomic status 40 , and sexual abuse 39 . No systematic reviews that analyzed the relationship between depression and QOL among high school and university students were found. The evaluation of QOL can contribute to preventive actions in the context of depression, since it is a multidimensional concept that covers well-being and satisfaction with different areas of life 41 , 42 , 43 .

Assessing the relationship between depression and QOL is important for a broader understanding of the nature of diseases people are exposed to 21 , 44 , 45 . Understanding how the different degrees of depression affect QOL and whether QOL interferes with the progression of the severity of depressive symptoms is necessary, since evidence shows that the trajectory of depressive symptoms vary within the same population 46 , 47 , 48 . Thus, the objectives of this study are: (1) to estimate the prevalence of depression among high school and university students and (2) to identify the relationship between depression and QOL among high school and university students through a systematic review of the literature and a meta-analysis. In addition, we aimed to summarize the evidence of the influence of depression and QOL on academic performance, absenteeism, and school dropout rates among these students. The consolidation of these findings is essential to identify and clarify the risk factors for depression among adolescents and young people. In this way, it will be possible to guide future research and interventions focusing on improving students' mental health.

Research questions

The main research questions guiding this systematic review are, “What is the prevalence of depression among high school and university students?” and “What is the evidence on the relationship between depression and QOL among high school and university students?” The secondary question guiding this review is “What are the influences of depression and QOL on academic performance, absenteeism, and school dropout rates among high school and university students?” If the high prevalence of depression among high school and university students is related to self-perception of quality of life, it is possible that this relationship is determined by specific dimensions of QOL and manifests itself in different ways among students.

Protocol and registration

The present systematic review was conducted according to the methodology for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 49 , for identification, screening, eligibility, and inclusion of studies. Details that are more specific can be found in the registration of the International Prospective Register of Systematic Reviews and in the published protocol article 50 . As the analysis was based on published articles (secondary data), ethical approval was not necessary.

This review follows the population, exposure, comparator, outcome (PECO) structure, mentioned in the recommended notification items for systematic reviews 51 . Thus, “P” represents high school and university students; “E”, depression and QOL; “C”, sex and age group; and “O”, depression and QOL 51 . Academic performance, absenteeism, and school dropout rates were also analyzed as secondary outcomes.

Search strategy and eligibility criteria

In January 2023, a researcher (reviewer 1) accessed the Scopus, Embase, PubMed, Scielo, CINAHL, and Web of Science databases, restricting the search to publications in English between 2011 and 2023. The choice to limit the search to the last 13 years was guided by the following factors: (a) focusing on recent publications in the area, particularly those that assessed depression based on the current criteria of the Diagnostic and Statistical Manual of Mental disorders (DSM-5), published in 2013 52 is more relevant, and (b) a prior analysis, based on PubMed, showed that publications and the production of research citations in this area were significantly increasing from 2011 onwards.

Table 1 shows the search strategy adapted to the different databases. The search strategy was also complemented by: (a) tracking of the references of the included studies and relevant systematic reviews, and (b) searches in Google Scholar. The main search keywords were: “high school students”, “college students” (population), “depression” (exposure/outcome) and “quality of life” (exposure/outcome).

Depression was defined as any depressive disorder based on a clinical diagnosis, according to the criteria of the International Statistical Classification of Diseases and Related Health Problems 53 , 54 or the DSM 52 , or by the evaluation of depressive symptoms through a validated inventory/self-reporting questionnaire 55 , 56 . QOL was defined, according to the criteria of the World Health Organization (WHO), as “individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” 57 .

Observational studies (cross-sectional and longitudinal) with the following characteristics were included: (a) a sample of high school and university students aged 10–33 years; (b) depression and QOL as the main outcome or exposure/risk factor; (c) reported the association between depression and QOL; (d) used a standardized questionnaire for QOL or health related QOL (HRQOL); and (e) evaluation of depression/depressive symptoms with validated instruments and/or clinical diagnosis. The age range 10 to 33 years was used based on the age of adolescents and young adults (age, 10 to 24 years as defined by WHO) 57 . The age was extended to 33 years because the average age of university students is higher in recent years.

The exclusion criteria were: (a) theses, dissertations, books, book chapters, reviews, case reports, comments, letters and editorials, duplicate articles, and articles in which the full text could not be retrieved in online databases, through library requests, or by e-mails sent to the author(s) of the study; (b) studies with specific populations (pregnant and breastfeeding women, victims of violence, amputees, inpatients, and disabled people; in disaster situations, athletes, asthmatics, diabetics, and hypertensive people; patients with HIV, cancer, arthritis, cystic fibrosis, among other chronic diseases); (c) studies with samples of mixed ages, unless data could be collected, organized or calculated separately; (d) incomplete data on the association between depression and QOL; (e) clinical trials and case–control studies; and (f) when more than one article provided data on the same sample.

Training of researchers

Before beginning the screening process, the researchers who participated in the eligibility assessments were subjected to training as to the inclusion/exclusion criteria of the study, with a practical session on eligibility assessment of 50 abstracts 58 . In addition, the researchers participated in another training session to standardize the risk of bias and the analysis of Newcastle–Ottawa Scale (NOS), evaluating five articles not included in the present study. Finally, the researchers were trained on how to correctly use the Rayyan software and standardize the procedures 58 .

Review process

After the bibliographic search, the articles retrieved in the databases were compared and the duplicates removed using EndNote X9 (Clarivate, PA, USA). In the first phase of the review, two researchers (reviewer 1 and reviewer 2) independently sorted the titles and summaries of all articles that met the inclusion and exclusion criteria. This phase was performed using Rayyan software (Rayyan Systems Inc., Cambridge, MA, USA) in blind mode 59 . Disagreements regarding the inclusion and exclusion criteria were discussed and resolved by a third researcher (reviewer 3). In the second phase, the selected articles were fully read by two researchers (reviewer 1 and reviewer 4) and evaluated to determine their eligibility. The reliability between evaluators for the inclusion and exclusion of the studies was determined by calculating the percentage of concordance and the Cohen’s kappa coefficient 58 . Finally, the eligible articles were included in the systematic review. The reference lists of the included articles were evaluated to identify possible additional studies lost in the database searches. Figure 1 shows the flowchart of this systematic review.

Flow diagram of the selection criteria for the study. Flowchart: Adapted from the PRISMA 2020 Flow Diagram.

Risk of bias and quality assessment of individual studies

The methodological quality and risk of bias among the studies were assessed by two researchers (reviewer 1 and reviewer 2) independently and with consensus. The methodological quality of the studies was evaluated using the online version of the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) tool 60 , 61 . The strength of evidence of the studies was classified into four categories: high (four circles filled), moderate (three circles filled), low (two circles filled), or very low (one circle filled) 60 , 61 . Factors such as the risk of bias, inconsistent results, indirect evidence, imprecision, and publication bias might decrease the quality of the evidence of the studies. However, the great magnitude of the effect, the dose–response gradient, and the presence of confounders in the reduction of the effect found are factors that could increase the quality of the evidence in the studies.

The NOS for observational studies 62 was used to assess the risk of bias. The adapted scale for cross-sectional (seven items) and cohort (eight items) studies consists of three dimensions that take into account the selection of participants, the comparability of the result groups, and the evaluation of the result measurements 38 . All studies could receive a maximum of one star for each item, except for comparability, in which up to two stars could be assigned. The studies were considered as having a low risk of bias (≥ 3 points) or high risk of bias (< 3 points) 38 . In addition, we assessed whether the authors provided a statement on conflicts of interest and information on ethical approval.

Data extraction and evidence synthesis

The following information was collected from the studies using a standard data extraction spreadsheet: authors, year of publication, site/country, study design, follow-up period (longitudinal studies), characteristics of the participants (sample size, sex, and age range/mean age), instruments for the assessment of depression with respective cutoff points, QOL evaluation instruments, main findings, and association values.

Data regarding the prevalence of depression and association measures were collected, in addition to other additional results that refer to factors associated with depression and QOL. The results were categorized into two groups: (a) high school students and (b) university students. Data were collected and evaluated by two independent researchers (reviewer 1 and reviewer 4) and disagreements were resolved by a third researcher (reviewer 2).

The prevalence of depression and the results of the association between depression and QOL among students are presented as the main outcomes. The results of the prevalence of depression in the studies analyzed were presented according to the intensity of depressive symptoms. The different QOL domains evaluated were also considered in synthesizing the evidence. Secondary results are presented, including additional variables that are associated with students’ depression and QOL. We also described whether the studies presented results on the influence of depression and QOL on academic performance, absenteeism, and evasion. When possible, the differences between the sexes and age groups in terms of the prevalence of depression and the level of QOL among the students were compared.

Meta-analysis

A meta-analysis was conducted using the random effects model with data on the prevalence of depression among high school students, depression among university students, and moderate and low QOL. The data are graphically displayed in Forest plots, showing prevalence rates with their 95% confidence intervals (CIs). Publication bias was evaluated using Egger’s test. All analyses were conducted using Stata version 16.0 (StataCorp LLC, College Station, TX, USA).

Literature search and study selection

Figure 1 shows the selection process for this systematic review. In all, 12,842 articles were identified based on the eligibility criteria, and 28 additional articles were identified through lists of references and manual searches. After excluding duplicate articles, 7,877 articles were selected for title and abstract reading. There was moderate agreement (agreement = 99.4%, kappa = 0.60) between researchers and 150 articles remained for full text evaluation. After the full text analysis, 36 studies met the eligibility criteria and were included in the systematic review (Fig. 1 ). The articles included analyzed depression and QOL among high school and university students and provided information on the relationship between depression and QOL (Table 2 ) 44 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 .

Risk of bias and quality of the evidence

The NOS scale scores ranged from three to nine points. The classification of studies with lower scores 44 , 67 , 70 , 81 was related to unclear description of confounding factors, unadjusted results for confounders, and comparability between respondents and non-respondents characteristic. All studies reached scores ≥ 3 and were evaluated as having low risk of bias (Table 2 ).

The strength of the evidence classified using the GRADE methodology indicated that the studies had low (n = 19, 53%), moderate (n = 13, 36%), and high (n = 4, 11%) quality (Table 2 ). The low and moderate quality was justified by the inaccuracy of the results of observational studies, the reduced sample size, and the effect produced by these studies. Seven studies 67 , 70 , 71 , 81 , 88 , 90 , 95 , 97 did not clearly specify conflicts of interest, and two studies did not report whether ethical approval was obtained 71 , 89 (Table 2 ).

Characteristics of the studies

Table 3 presents the characteristics of the studies included in the review, grouped into the following categories: year of publication, region, study design, students' study modality, sample size and types of assessment instruments for depressive symptoms/depression and QOL. This review included studies of students of 20 nationalities and a total sample of 24,704 people. Most studies were published between 2014 and 2020 (n = 20, 55.6%), mainly with the Asian population (n = 21, 58.3%), and university students (n = 27, 75%). With the exception of a single study, all studies included samples of both sexes. The study design mainly covered cross-sectional studies (n = 15, 93.8%), with only one longitudinal study 93 . The sample size ranged from 40 participants 88 to 4,467 participants 92 , 75.0% of whom were university students (Table 3 ). The mean age of high school students ranged from 13.2 (± 2.1) 70 to 16.9 (± 1.2) years 92 , while the mean age of university students ranged from 19.0 (± 1.1) 63 to 22.8 (± 3.0) years 63 . Most of the studies included a sample of medical students 63 , 64 , 65 , 67 , 76 , 79 , 81 , 82 , 87 , 89 , 93 , 96 nursing students 80 , 95 , and health students 68 , 73 , 78 , 85 , 88 , 94 . Only six studies included a large sample of university students 44 , 66 , 69 , 84 , 86 , 97 . No study evaluated the possible influences of depression and QOL on academic performance, absenteeism, and school dropout.

Characteristics of results and main findings

The characteristics and main results are presented separately for the evaluation of depression and QOL among students, prevalence of depression and its relationship with QOL among students, other factors associated with depression and QOL among students, and meta-analysis.

Evaluation of depression and quality of life among students

Table 3 shows a summary of the instruments used to assess depressive symptoms and Table 4 lists the respective cutoff points adopted in each study. The most widely used instrument for assessing depression and depressive symptoms was the Beck Depression Inventory (BDI) (n = 9, 25.0%), with cutoff points ranging from ≥ 10 to > 15 for the presence of depressive symptoms. Other studies used a variety of instruments to assess depression and depressive symptoms, including the Depression Anxiety Stress Scale (DASS-21) (n = 6, 16.7%) and the Zung Self-Rating Depression Scale (ZUNG SDS) (n = 2, 5.5%) 65 , 85 .

Twelve studies did not specify the cutoff points adopted for the evaluation of depressive symptoms 44 , 68 , 69 , 70 , 76 , 79 , 82 , 89 , 91 , 92 , 93 , 97 . There were no studies based on the clinical diagnosis of depression, and the evaluation of depressive symptoms is prevalent through self-reporting questionnaires. The severity of depressive symptoms was evaluated only in eight studies 64 , 65 , 67 , 83 , 84 , 90 , 96 , 97 , in which the prevalence of depressive symptoms was categorized into mild, moderate, and severe/significant symptoms.

For the QOL evaluation, the most widely used instrument was the World Health Organization QOL Questionnaire (WHOQOL; WHOQOL-BREF) (n = 19, 52.8%), followed by the RAND 36-item Short Form Survey (SF-36) (n = 7, 19.4%), as specified in Table 3 . The different QOL domains evaluated by the main instruments covered the physical, environmental, psychological, and social domains (WHOQOL; WHOQOL-BREF, and SF-36), and the sub-domains related to functional capacity, general health perceptions, bodily pain, vitality, social, physical, and mental functioning, and limitations caused by emotional problems (SF-36). Although there was a certain tendency for studies to assess QOL from different domains, ten studies did not analyze these domains/sub-domains 44 , 66 , 68 , 71 , 72 , 74 , 78 , 85 , 88 , 89 .

Prevalence of depression and its relation to students’ quality of life

Table 4 shows a summary of the results on the prevalence of depression and its relationship with the students’ QOL, categorized by high school and university students, by the intensity of depressive symptoms and by instruments used in the evaluation of depression and QOL. The prevalence of depressive symptoms among high school students ranged from 8.5% among French students 71 to 43.4% among Brazilian students 77 . Among college students, the prevalence of depressive symptoms ranged from 3.3% among Indonesian students 81 to 61% among Malaysian and Brazilian students 83 , 96 . Table 5 shows the main results on the relationship between depression and QoL. Association/correlation tests for each study can be found in Supplementary File 1 .

Studies with a sample of high school students identified that QoL is negatively correlated with depression (n = 8, 100%). Only one study showed that, regarding the QoL domains, the financial resources and social support dimensions were not correlated with depression among students from Mexico 75 . In general, studies with a sample of university students found that depression is associated with low QoL (n = 11, 40.7%). In addition, depression was a predictor of QoL and vice versa. On the other hand, other studies (n = 6, 22.2%) present a varied behavior regarding the relationship between different QOL domains and the prevalence of depressive symptoms. In Thai and Malaysian students, for example, depression was associated only with the psychological and physical domains of QOL 64 , 67 , while a study with a sample of 193 Brazilian students indicated that the physical domain of QOL was unaffected by depression 96 . In two studies depression is not correlated with QOL 68 , 85 .

Three studies analyzed the relationship between depressive symptoms and QOL among German, Brazilian and Pakistani students with a longitudinal design 78 , 88 , 93 . The German students showed an increase in depression symptoms over the semesters, with highly significant correlations between depression and mental quality of life 78 . The presence of depressive symptoms among Brazilian students was negatively related to QOL in all domains, except for the physical domain 93 . It also showed that students with depression at the beginning of graduation tend to maintain depressive symptoms over time, contributing to a worse future QOL 93 . Female students were more likely to have a worse physical QOL over time 93 . On the other hand, students with depression showed improvement in QoL during the COVID-19 epidemic lockdown in Pakistan 88 .

Other factors associated with depression and quality of life among students

In addition to the main results of interest, the studies presented other important variables that are associated with depression and QOL among students, such as anxiety and academic stress. According to one study, self-esteem was positively correlated with QOL, while anxiety symptoms, and relationship with their parents were negatively correlated with QOL in high school students 92 . Another study analyzed that QOL was also correlated with low and moderate anxiety, with a high level of general well-being and with low/moderate level of educational stress 74 .

Studies have shown that among university students, QOL was negatively correlated with anxiety 44 , 67 , 94 and emotional control 44 , and positively correlated with general positive affection, emotional bonds, life satisfaction 44 , and family income 97 . Students who engaged in physical activity every day had higher scores on the HRQOL 97 .

The frequency of depressive symptoms increased with increased anxiety 63 , 85 , academic stress, sleep disorders, academic pressure 66 , and perceived stress 85 . Students with depression had higher scores for social phobia 63 and the intensity of depressive symptoms was higher in the last year of their undergraduate course 95 . In a sample of Chinese students, depression was more prevalent among medical students, followed by engineering and arts students 69 .

Seven studies evaluated depression and QOL of students during the COVID-19 pandemic 70 , 73 , 75 , 77 , 81 , 84 , 88 . In the pandemic period, the prevalence of depression ranged from 21.2% among Mexican high school students 75 to 57.9% among Indonesian university students 84 . It was observed that the COVID-19 pandemic negatively affected the mental health and QOL of students 73 , 88 and that depression symptoms were associated with poor quality of life and social isolation 70 , 75 , 77 , 81 , 88 .

Figure 2 shows the combined prevalence of depression among high school students and depression among university students. The combined prevalence of depression among students was 27% (95% CI 0.21–0.33). The prevalence of depression among High school students was 25% (95% CI 0.14–0.37). The prevalence of depression among university students was 27% (95% CI 0.20–0.34).

Forest plot evaluating the prevalence of depression in students, using data from 26 studies. Flowchart: Elaborated by the authors.

There was a high level of statistical heterogeneity ( I 2 = 99.40%, p < 0.001). Heterogeneity had an influence on the result of the analysis. Evidence of publication bias in the meta-analysis of the combined prevalence was found using the Egger’s regression test ( p = 0.000).

In the meta-analysis, involving three studies, the odds ratio for the association between depression and quality of life in students was 0.009 (95% CI − 0.009 to 0.027), ( I 2 = 95.6%, p < 0.01), not indicating a positive association 68 , 74 , 85 .

The present study systematically estimated the prevalence of depression and summarized the relationship between depression and QOL among high school and university students. The prevalence of depressive symptoms was 27% among students and most studies have shown that depressive symptoms was associated with a low QOL. Despite being relevant to research involving students, the studies did not evaluate the influence of depression and QOL on academic performance, absenteeism, and school dropout rates.

The main results show that the estimated prevalence rate of depression among university students was 27%, similar to the results of other meta-analyses that present the prevalence of depressive symptoms of 24.4% to 34.0% with the same population 11 , 35 , 36 , 38 , 40 . About 25% of high school students had depressive symptoms. Indonesian and Brazilian high school students had a higher prevalence of depressive symptoms compared to students from Mexico, Republic of Korea and France. Differences in the prevalence of depression can also be observed in different studies, where the prevalence of depression was in Chinese, 24.3% 12 , Pakistani (17.2%), and Malaysian (26.2%) students 98 , 99 . However, high school students in Indonesia had a higher prevalence of depressive symptoms, with rates of 52.7% 100 .

The findings of this review also demonstrate that high school and university students present a higher prevalence of depressive symptoms compared to large samples in distinct communities, ranging from 7.3% in countries like Australia to 20.6% in South American countries 101 . Estimates of a 12-month depression prevalence in adolescents and young adults in the United States range from 8.7% to 11.3% 102 , rates lower compared to those found in the present review.

The manifestations of depressive symptoms are not static, and they affect a distinct population of students 45 , 93 , since there are several biological, psychological, and social factors that contribute to the risk of depression, including cultural determinants that are present in the person’s life such as the context of development, parental practices, and temperament 48 , 98 . Part of the challenge relates to the heterogeneous nature of the diagnosis and condition of depression. There is an emerging notion that mood disorders lie on a spectrum 103 . In addition, individuals of different ethnicities may express depression differently. Chinese, for example, tend to deny mental health symptoms or express them somatically 104 . Given the complexity of identifying protection mechanisms and risk factors, research suggests that the dimensions of subjective well-being are complementary aspects of the evaluation of depression symptoms 25 , 105 , 106 . In addition, QOL is an important indicator for identifying groups vulnerable to depressive symptoms and the golden objective for treating depression is to improve QOL 21 .

In this review, 97.2% of the studies showed some type of association between depression and QOL, indicating that students with depressive symptoms tend to have worse QOL, or that QOL is a predictor of depression. The role of depressive symptoms as a negative predictor of QOL was documented in other reviews with adolescents 9 and university students 107 . However, the main relevance of the present study is the fact that depressive symptoms may not impact in the same way in the different domains of QOL 64 , 67 , 93 , 96 . The psychological dimension of the QOL of students seems to be the most affected; however, it is not possible to state precisely that it does not occur with the physical, environmental, and social dimensions of the QOL. This is because other factors associated with depression and QOL must be considered, such as the presence of chronic or physical diseases, for example 108 .

Data from the meta-analysis indicate that there is no positive association between depression and QOL in students, showing a possible influence of other mediators on the relationship between depression and QOL. Some people, despite experiencing depressive symptoms at some stage of life, may present adaptive mechanisms that allow them to self-manage mental suffering and demonstrate resilience 32 , 43 , 98 , 109 , 110 , 111 , 112 . The influence of different degrees of depressive symptoms may also compromise the analysis of results, but studies do not provide enough data to support this statement. Therefore, these findings are limited in clarifying the wide and complex relationship between depression and QOL among students. Further studies are needed, mainly with longitudinal design and with quality evidence.

With regard to QOL, the perception of QOL can be more positive or negative as for the meanings each person attributes to their life experiences 111 , 113 , 114 , 115 , 116 To better understand these aspects, the evaluation of QOL should consider the relationship between positive and negative psychological dimensions as independent but at the same time inter-related dimensions 25 . In this sense, a favorable educational environment may play a “barrier” role in negative psychological dimensions among students, such as stress 25 . The psychological, physical, environmental, and social domains of QOL present important differences when analyzed in terms of sex and geographic region 64 , 93 , 95 . Female students tend to present worse QOL, in addition to having the most impaired physical domain of QOL 93 , 96 , 117 , a condition that may be associated with the probability of women exercising less than men 118 . This can also be explained by the fact that different instruments are used in the evaluation of QOL and by adverse cultural or social factors.

This study also showed that students experienced intense depressive symptoms and worsened QOL during the COVID-19 pandemic. Since the establishment of social distancing/isolation measures due to the COVID-19 pandemic caused by the SARS-CoV-2 virus, students have shown considerable increases in depressive symptoms and anxiety 119 , 120 . In part, this is due to prolonged social isolation, bereavement, violence in the family context, and excessive use of the internet and social networks 121 , 122 , 123 , 124 , 125 , 126 . The existence of social distancing implemented to prevent the spread of the COVID-19 virus caused limitations in physical and social activities, including leisure activities and in the sufficiency of the family's financial 127 . In addition, the blockade and closure of schools and universities forced students to study at home, which may have contributed to increased symptoms of depression and consequent worsening of QOL 127 , 128 .

This review had some limitations. First, the assessment of depression and QOL in the studies considered different instruments, which made comparison of results difficult. Second, the most widely used instrument for the evaluation of depressive symptoms, the BDI, presented different cutoff points in the selected studies, which may reflect probable bias. In addition, screening tools are criticized for having a greater chance of false-positive results, making the burden of the disease seem worse 129 . Depressive symptoms were measured using psychometric tools that indicated the presence or absence of symptoms, but they were not able to diagnose depression. A clinical evaluation would be essential to better understand and standardize the results 21 , 42 . Third, most studies used a cross-sectional design, which does not allow definitive conclusions on causality. Longitudinal studies could demonstrate whether poor QOL is a predictor of depression or whether depression is a predictor of low QOL, in addition to clarifying how the intensity of depressive symptoms interacts with QOL and vice-versa. Fourth, the results cannot be generalized since most participants are medical, nursing and health students. Fifth, excluding gray research sources from our systematic review may resulted in loss of information on the subject. So, for future studies, we suggest to take into account the possibility to include a gray literature search as a step of the search strategy. Finally, the studies did not analyze important factors mediating in the relationship between QOL and mental health, such as socioeconomic level, stress, coping style, and personality 112 , 130 , 131 .

The strengths of this study include the specific assessment of depression, to the detriment of a wide scope of mental health problems, which allows a particular analysis of its relationship with QOL. Results from the analysis of conflicts of interest and ethical approvals, which are often omitted from the assessments, are also presented here. A meta-analysis was conducted to provide a general estimate of the prevalence of depression among high school and university students. To the best of our knowledge, this is the first systematic review that summarizes the evidence on the relationship between depression and QOL among high school and university students, allowing us to clarify the gaps in the literature and propose recommendations for future research. In addition, this is the first study that intended to analyze academic consequences, such as academic performance, absenteeism, and school dropout. However, the studies included in this review did not analyze these aspects, which indicate a lack of research on the academic consequences, from the perspective of the relationship between depression and QOL.

New studies should be conducted considering the severity, duration, and patterns of depressive symptoms in high school and university students, to better understand the relationship between depression and QOL. Future research directions also include in-depth study on the relationship between depressive symptoms and specific dimensions of QOL, considering its domains and sub-domains, identification of sociodemographic variables and the influence of coping mechanisms on the relationship between depression and QOL, and longitudinal assessment of the relationship between depression and QOL among students. Health professionals and education professionals must better understand the different aspects of the life of students who are depressed, being able to determine its origin and the protection mechanisms that can be used in punctual interventions 68 , 131 .

Depression is associated with the QOL of students; however, the relationship between depression and QOL is not clear yet. There is a need to understand whether QOL can affect the nature, duration, and intensity of depressive symptoms and the real impact of depressive symptoms on different QOL domains. The consolidation of these findings is fundamental to a more effective and integrated orientation of public health and education policies, focusing on promoting mental health and improving the students’ QOL. The multidimensional aspect that refers to the students’ mental health and QOL should be considered from a multidisciplinary and global conception, with the participation of health professionals, education professionals and the family in social and instrumental support, thus contributing to students’ academic performance and success.

Data availability

Due to sensitive data, the data can be accessed upon request to the authors ([email protected] (MSVF); [email protected] (MN)).

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The authors thank the Federal Institute of Education, Science and Technology of Goiano (IF Goiano) for funding this research, and the Child and Adolescent Health Research Group (GPSaCA).

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Michele da Silva Valadão Fernandes, Priscilla Rayanne e Silva Noll & Matias Noll

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Carolina Rodrigues Mendonça

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Thays Martins Vital da Silva

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Published: 20 September 2022

Factors that influence mental health of university and college students in the UK: a systematic review

Fiona Campbell 1 ,
Lindsay Blank 1 ,
Anna Cantrell 1 ,
Susan Baxter 1 ,
Christopher Blackmore 1 ,
Jan Dixon 1 &
Elizabeth Goyder 1

BMC Public Health volume 22 , Article number: 1778 ( 2022 ) Cite this article

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Worsening mental health of students in higher education is a public policy concern and the impact of measures to reduce transmission of COVID-19 has heightened awareness of this issue. Preventing poor mental health and supporting positive mental wellbeing needs to be based on an evidence informed understanding what factors influence the mental health of students.

To identify factors associated with mental health of students in higher education.

We undertook a systematic review of observational studies that measured factors associated with student mental wellbeing and poor mental health. Extensive searches were undertaken across five databases. We included studies undertaken in the UK and published within the last decade (2010–2020). Due to heterogeneity of factors, and diversity of outcomes used to measure wellbeing and poor mental health the findings were analysed and described narratively.

We included 31 studies, most of which were cross sectional in design. Those factors most strongly and consistently associated with increased risk of developing poor mental health included students with experiences of trauma in childhood, those that identify as LGBTQ and students with autism. Factors that promote wellbeing include developing strong and supportive social networks. Students who are prepared and able to adjust to the changes that moving into higher education presents also experience better mental health. Some behaviours that are associated with poor mental health include lack of engagement both with learning and leisure activities and poor mental health literacy.

Improved knowledge of factors associated with poor mental health and also those that increase mental wellbeing can provide a foundation for designing strategies and specific interventions that can prevent poor mental health and ensuring targeted support is available for students at increased risk.

Peer Review reports

Poor mental health of students in further and higher education is an increasing concern for public health and policy [ 1 , 2 , 3 , 4 ]. A 2020 Insight Network survey of students from 10 universities suggests that “1 in 5 students has a current mental health diagnosis” and that “almost half have experienced a serious psychological issue for which they felt they needed professional help”—an increase from 1 in 3 in the same survey conducted in 2018 [ 5 ]. A review of 105 Further Education (FE) colleges in England found that over a three-year period, 85% of colleges reported an increase in mental health difficulties [ 1 ]. Depression and anxiety were both prevalent and widespread in students; all colleges reported students experiencing depression and 99% reported students experiencing severe anxiety [ 5 , 6 ]. A UK cohort study found that levels of psychological distress increase on entering university [ 7 ], and recent evidence suggests that the prevalence of mental health problems among university students, including self-harm and suicide, is rising, [ 3 , 4 ] with increases in demand for services to support student mental health and reports of some universities finding a doubling of the number of students accessing support [ 8 ]. These common mental health difficulties clearly present considerable threat to the mental health and wellbeing of students but their impact also has educational, social and economic consequences such as academic underperformance and increased risk of dropping out of university [ 9 , 10 ].

Policy changes may have had an influence on the student experience, and on the levels of mental health problems seen in the student population; the biggest change has arguably been the move to widen higher education participation and to enable a more diverse demographic to access University education. The trend for widening participation has been continually rising since the late 1960s [ 11 ] but gained impetus in the 2000s through the work of the Higher Education Funding Council for England (HEFCE). Macaskill (2013) [ 12 ] suggests that the increased access to higher education will have resulted in more students attending university from minority groups and less affluent backgrounds, meaning that more students may be vulnerable to mental health problems, and these students may also experience greater challenges in making the transition to higher education.

Another significant change has been the introduction of tuition fees in 1998, which required students to self fund up to £1,000 per academic year. Since then, tuition fees have increased significantly for many students. With the abolition of maintenance grants, around 96% of government support for students now comes in the form of student loans [ 13 ]. It is estimated that in 2017, UK students were graduating with average debts of £50,000, and this figure was even higher for the poorest students [ 13 ]. There is a clear association between a student’s mental health and financial well-being [ 14 ], with “increased financial concern being consistently associated with worse health” [ 15 ].

The extent to which the increase in poor mental health is also being seen amongst non-students of a similar age is not well understood and warrants further study. However, the increase in poor mental health specifically within students in higher education highlights a need to understand what the risk factors are and what might be done within these settings to ensure young people are learning and developing and transitioning into adulthood in environments that promote mental wellbeing.

Commencing higher education represents a key transition point in a young person’s life. It is a stage often accompanied by significant change combined with high expectations of high expectations from students of what university life will be like, and also high expectations from themselves and others around their own academic performance. Relevant factors include moving away from home, learning to live independently, developing new social networks, adjusting to new ways of learning, and now also dealing with the additional greater financial burdens that students now face.

The recent global COVID-19 pandemic has had considerable impact on mental health across society, and there is concern that younger people (ages 18–25) have been particularly affected. Data from Canada [ 16 ] indicate that among survey respondents, “almost two-thirds (64%) of those aged 15 to 24 reported a negative impact on their mental health, while just over one-third (35%) of those aged 65 and older reported a negative impact on their mental health since physical distancing began” (ibid, p.4). This suggests that older adults are more prepared for the kind of social isolation which has been brought about through the response to COVID-19, whereas young adults have found this more difficult to cope with. UK data from the National Union of Students reports that for over half of UK students, their mental health is worse than before the pandemic [ 17 ]. Before COVID-19, students were already reporting increasing levels of mental health problems [ 2 ], but the COVID-19 pandemic has added a layer of “chronic and unpredictable” stress, creating the perfect conditions for a mental health crisis [ 18 ]. An example of this is the referrals (both urgent and routine) of young people with eating disorders for treatment in the NHS which almost doubled in number from 2019 to 2020 [ 19 ]. The travel restrictions enforced during the pandemic have also impacted on student mental health, particularly for international students who may have been unable to commence studies or go home to see friends and family during holidays [ 20 ].

With the increasing awareness and concern in the higher education sector and national bodies regarding student mental health has come increasing focus on how to respond. Various guidelines and best practice have been developed, e.g. ‘Degrees of Disturbance’ [ 21 ], ‘Good Practice Guide on Responding to Student Mental Health Issues: Duty of Care Responsibilities for Student Services in Higher Education’ [ 22 ] and the recent ‘The University Mental Health Charter’ [ 2 ]. Universities UK produced a Good Practice Guide in 2015 called “Student mental wellbeing in higher education” [ 23 ]. An increasing number of initiatives have emerged that are either student-led or jointly developed with students, and which reflect the increasing emphasis students and student bodies place on mental health and well-being and the increased demand for mental health support: Examples include: Nightline— www.nightline.ac.uk , Students Against Depression— www.studentsagainstdepression.org , Student Minds— www.studentminds.org.uk/student-minds-and-mental-wealth.html and The Alliance for Student-Led Wellbeing— www.alliancestudentwellbeing.weebly.com/ .

Although requests for professional support have increased substantially [ 24 ] only a third of students with mental health problems seek support from counselling services in the UK [ 12 ]. Many students encounter barriers to seeking help such as stigma or lack of awareness of services [ 25 ], and without formal support or intervention, there is a risk of deterioration. FE colleges and universities have identified the need to move beyond traditional forms of support and provide alternative, more accessible interventions aimed at improving mental health and well-being. Higher education institutions have a unique opportunity to identify, prevent, and treat mental health problems because they provide support in multiple aspects of students’ lives including academic studies, recreational activities, pastoral and counselling services, and residential accommodation.

In order to develop services that better meet the needs of students and design environments that are supportive of developing mental wellbeing it is necessary to explore and better understand the factors that lead to poor mental health in students.

Research objectives

The overall aim of this review was to identify, appraise and synthesise existing research evidence that explores the aetiology of poor mental health and mental wellbeing amongst students in tertiary level education. We aimed to gain a better understanding of the mechanisms that lead to poor mental health amongst tertiary level students and, in so doing, make evidence-based recommendations for policy, practice and future research priorities. Specific objectives in line with the project brief were to:

To co-produce with stakeholders a conceptual framework for exploring the factors associated with poorer mental health in students in tertiary settings. The factors may be both predictive, identifying students at risk, or causal, explaining why they are at risk. They may also be protective, promoting mental wellbeing.

To conduct a review drawing on qualitative studies, observational studies and surveys to explore the aetiology of poor mental health in students in university and college settings and identify factors which promote mental wellbeing amongst students.

To identify evidence-based recommendations for policy, service provision and future research that focus on prevention and early identification of poor mental health

Methodology

Identification of relevant evidence.

The following inclusion criteria were used to guide the development of the search strategy and the selection of studies.

We included students from a variety of further education settings (16 yrs + or 18 yrs + , including mature students, international students, distance learning students, students at specific transition points).

Universities and colleges in the UK. We were also interested in the context prior to the beginning of tertiary education, including factors during transition from home and secondary education or existing employment to tertiary education.

Any factor shown to be associated with mental health of students in tertiary level education. This included clinical indicators such as diagnosis and treatment and/or referral for depression and anxiety. Self-reported measures of wellbeing, happiness, stress, anxiety and depression were included. We did not include measures of academic achievement or engagement with learning as indicators of mental wellbeing.

Study design

We included cross-sectional and longitudinal studies that looked at factors associated with mental health outcomes in Table 5 .

Data extraction and quality appraisal

We extracted and tabulated key data from the included papers. Data extraction was undertaken by one reviewer, with a 10% sample checked for accuracy and consistency The quality of the included studies were evaluated using the Newcastle-Ottawa Scale [ 26 ] and the findings of the quality appraisal used in weighting the strength of associations and also identifying gaps for future high quality research.

Involvement of stakeholders

We recruited students, ex-students and parents of students to a public involvement group which met on-line three times during the process of the review and following the completion of the review. During a workshop meeting we asked for members of the group to draw on their personal experiences to suggest factors which were not mentioned in the literature.

Methods of synthesis

We undertook a narrative synthesis [ 27 ] due to the heterogeneity in the exposures and outcomes that were measured across the studies. Data showing the direction of effects and the strength of the association (correlation coefficients) were recorded and tabulated to aid comparison between studies.

Search strategy

Searches were conducted in the following electronic databases: Medline, Applied Social Sciences Index and Abstracts (ASSIA), International Bibliography of Social Sciences (IBSS), Science,PsycINFO and Science and Social Sciences Ciatation Indexes. Additional searches of grey literature, and reference lists of included studies were also undertaken.

The search strategy combined a number of terms relating to students and mental health and risk factors. The search terms included both subject (MeSH) and free-text searches. The searches were limited to papers about humans in English, published from 2010 to June 2020. The flow of studies through the review process is summarised in Fig. 1 .

Flow diagram

The full search strategy for Medline is provided in Appendix 1 .

Thirty-one quantitative, observational studies (39 papers) met the inclusion criteria. The total number of students that participated in the quantitative studies was 17,476, with studies ranging in size from 57 to 3706. Eighteen studies recruited student participants from only one university; five studies (10 publications) [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ] included seven or more universities. Six studies (7 publications) [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ] only recruited first year students, while the majority of studies recruited students from a range of year groups. Five studies [ 39 , 42 , 43 , 44 , 45 ] recruited only, or mainly, psychology students which may impact on the generalisability of findings. A number of studies focused on students studying particular subjects including: nursing [ 46 ] medicine [ 47 ], business [ 48 ], sports science [ 49 ]. One study [ 50 ] recruited LGBTQ (lesbian, gay, bisexual, transgender, intersex, queer/questioning) students, and one [ 51 ] recruited students who had attended hospital having self-harmed. In 27 of the studies, there were more female than male participants. The mean age of the participants ranged from 19 to 28 years. Ethnicity was not reported in 19 of the studies. Where ethnicity was reported, the proportion that were ‘white British’ ranged from 71 – 90%. See Table 1 for a summary of the characteristics of the included studies and the participants.

Design and quality appraisal of the included studies

The majority of included studies ( n = 22) were cross-sectional surveys. Nine studies (10 publications) [ 35 , 36 , 39 , 41 , 43 , 50 , 51 , 52 , 53 , 62 ] were longitudinal in design, recording survey data at different time points to explore changes in the variables being measured. The duration of time that these studies covered ranged from 19 weeks to 12 years. Most of the studies ( n = 22) only recruited participants from a single university. The use of one university setting and the large number of studies that recruited only psychology students weakens the wider applicability of the included studies.

Quantitative variables

Included studies ( n = 31) measured a wide range of variables and explored their association with poor mental health and wellbeing. These included individual level factors: age, gender, sexual orientation, ethnicity and a range of psychological variables. They also included factors that related to mental health variables (family history, personal history and mental health literacy), pre-university factors (childhood trauma and parenting behaviour. University level factors including social isolation, adjustment and engagement with learning. Their association was measured against different measures of positive mental health and poor mental health.

Measurement of association and the strength of that association has some limitations in addressing our research question. It cannot prove causality, and nor can it capture fully the complexity of the inter-relationship and compounding aspect of the variables. For example, the stress of adjustment may be manageable, until it is combined with feeling isolated and out of place. Measurement itself may also be misleading, only capturing what is measureable, and may miss variables that are important but not known. We included both qualitative and PPI input to identify missed but important variables.

The wide range of variables and different outcomes, with few studies measuring the same variable and outcomes, prevented meta-analyses of findings which are therefore described narratively.

The variables described were categorised during the analyses into the following categories:

Vulnerabilities – factors that are associated with poor mental health

Individual level factors including; age, ethnicity, gender and a range of psychological variables were all measured against different mental health outcomes including depression, anxiety, paranoia, and suicidal behaviour, self-harm, coping and emotional intelligence.

Six studies [ 40 , 42 , 47 , 50 , 60 , 63 ] examined a student’s ages and association with mental health. There was inconsistency in the study findings, with studies finding that age (21 or older) was associated with fewer depressive symptoms, lower likelihood of suicide ideation and attempt, self-harm, and positively associated with better coping skills and mental wellbeing. This finding was not however consistent across studies and the association was weak. Theoretical models that seek to explain this mechanism have suggested that older age groups may cope better due to emotion-regulation strategies improving with age [ 67 ]. However, those over 30 experienced greater financial stress than those aged 17-19 in another study [ 63 ].

Sexual orientation

Four studies [ 33 , 40 , 64 , 68 ] examined the association between poor mental health and sexual orientation status. In all of the studies LGBTQ students were at significantly greater risk of mental health problems including depression [ 40 ], anxiety [ 40 ], suicidal behaviour [ 33 , 40 , 64 ], self harm [ 33 , 40 , 64 ], use of mental health services [ 33 ] and low levels of wellbeing [ 68 ]. The risk of mental health problems in these students compared with heterosexual students, ranged from OR 1.4 to 4.5. This elevated risk may reflect the greater levels of isolation and discrimination commonly experienced by minority groups.

Nine studies [ 33 , 38 , 39 , 40 , 42 , 47 , 50 , 60 , 63 ] examined whether gender was associated mental health variables. Two studies [ 33 , 47 ] found that being female was statistically significantly associated with use of mental health services, having a current mental health problem, suicide risk, self harm [ 33 ] and depression [ 47 ]. The results were not consistent, with another study [ 60 ] finding the association was not significant. Three studies [ 39 , 40 , 42 ] that considered mediating variables such as adaptability and coping found no difference or very weak associations.

Two studies [ 47 , 60 ] examined the extent to which ethnicity was associated with mental health One study [ 47 ] reported that the risks of depression were significantly greater for those who categorised themselves as non-white (OR 8.36 p = 0.004). Non-white ethnicity was also associated with poorer mental health in another cross-sectional study [ 63 ]. There was no significant difference in the McIntyre et al. (2018) study [ 60 ]. The small number of participants from ethnic minority groups represented across the studies means that this data is very limited.

Family factors

Six studies [ 33 , 40 , 42 , 50 , 60 ] explored the association of a concept that related to a student’s experiences in childhood and before going to university. Three studies [ 40 , 50 , 60 ] explored the impact of ACEs (Adverse Childhood Experiences) assessed using the same scale by Feletti (2009) [ 69 ] and another explored the impact of abuse in childhood [ 46 ]. Two studies examined the impact of attachment anxiety and avoidance [ 42 ], and parental acceptance [ 46 , 59 ]. The studies measured different mental health outcomes including; positive and negative affect, coping, suicide risk, suicide attempt, current mental health problem, use of mental health services, psychological adjustment, depression and anxiety.

The three studies that explored the impact of ACE’s all found a significant and positive relationship with poor mental health amongst university students. O’Neill et al. (2018) [ 50 ] in a longitudinal study ( n = 739) showed that there was in increased likelihood in self-harm and suicidal behaviours in those with either moderate or high levels of childhood adversities (OR:5.5 to 8.6) [ 50 ]. McIntyre et al. (2018) [ 60 ] ( n = 1135) also explored other dimensions of adversity including childhood trauma through multiple regression analysis with other predictive variables. They found that childhood trauma was significantly positively correlated with anxiety, depression and paranoia (ß = 0.18, 0.09, 0.18) though the association was not as strong as the correlation seen for loneliness (ß = 0.40) [ 60 ]. McLafferty et al. (2019) [ 40 ] explored the compounding impact of childhood adversity and negative parenting practices (over-control, overprotection and overindulgence) on poor mental health (depression OR 1.8, anxiety OR 2.1 suicidal behaviour OR 2.3, self-harm OR 2.0).

Gaan et al.’s (2019) survey of LGBTQ students ( n = 1567) found in a multivariate analyses that sexual abuse, other abuse from violence from someone close, and being female had the highest odds ratios for poor mental health and were significantly associated with all poor mental health outcomes [ 33 ].

While childhood trauma and past abuse poses a risk to mental health for all young people it may place additional stresses for students at university. Entry to university represents life stage where there is potential exposure to new and additional stressors, and the possibility that these students may become more isolated and find it more difficult to develop a sense of belonging. Students may be separated for the first time from protective friendships. However, the mechanisms that link childhood adversities and negative psychopathology, self-harm and suicidal behaviour are not clear [ 40 ]. McLafferty et al. (2019) also measured the ability to cope and these are not always impacted by childhood adversities [ 40 ]. They suggest that some children learn to cope and build resilience that may be beneficial.

McLafferty et al. (2019) [ 40 ] also studied parenting practices. Parental over-control and over-indulgence was also related to significantly poorer coping (OR -0.075 p < 0.05) and this was related to developing poorer coping scores (OR -0.21 p < 0.001) [ 40 ]. These parenting factors only became risk factors when stress levels were high for students at university. It should be noted that these studies used self-report, and responses regarding views of parenting may be subjective and open to interpretation. Lloyd et al.’s (2014) survey found significant positive correlations between perceived parental acceptance and students’ psychological adjustment, with paternal acceptance being the stronger predictor of adjustment.

Autistic students may display social communication and interaction deficits that can have negative emotional impacts. This may be particularly true during young adulthood, a period of increased social demands and expectations. Two studies [ 56 ] found that those with autism had a low but statistically significant association with poor social problem-solving skills and depression.

Mental health history

Three studies [ 47 , 51 , 68 ] investigated mental health variables and their impact on mental health of students in higher education. These included; a family history of mental illness and a personal history of mental illness.

Students with a family history or a personal history of mental illness appear to have a significantly greater risk of developing problems with mental health at university [ 47 ]. Mahadevan et al. (2010) [ 51 ] found that university students who self-harm have a significantly greater risk (OR 5.33) of having an eating disorder than a comparison group of young adults who self-harm but are not students.

Buffers – factors that are protective of mental wellbeing

Psychological factors.

Twelve studies [ 29 , 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 , 64 ] assessed the association of a range of psychological variables and different aspects of mental wellbeing and poor mental health. We categorised these into the following two categories: firstly, psychological variables measuring an individual’s response to change and stressors including adaptability, resilience, grit and emotional regulation [ 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 ] and secondly, those that measure self-esteem and body image [ 29 , 64 ].

The evidence from the eight included quantitative studies suggests that students with psychological strengths including; optimism, self-efficacy [ 70 ], resilience, grit [ 58 ], use of positive reappraisal [ 49 ], helpful coping strategies [ 42 ] and emotional intelligence [ 41 , 46 ] are more likely to experience greater mental wellbeing (see Table 2 for a description of the psychological variables measured). The positive association between these psychological strengths and mental well-being had a positive affect with associations ranging from r = 0.2–0.5 and OR1.27 [ 41 , 43 , 46 , 49 , 54 ] (low to moderate strength of association). The negative associations with depressive symptoms are also statistically significant but with a weaker association ( r = -0.2—0.3) [ 43 , 49 , 54 ].

Denovan (2017a) [ 43 ] in a longitudinal study found that the association between psychological strengths and positive mental wellbeing was not static and that not all the strengths remained statistically significant over time. The only factors that remained significant during the transition period were self-efficacy and optimism, remaining statistically significant as they started university and 6 months later.

Parental factors

Only one study [ 59 ] explored family factors associated with the development of psychological strengths that would equip young people as they managed the challenges and stressors encountered during the transition to higher education. Lloyd et al. (2014) [ 59 ] found that perceived maternal and paternal acceptance made significant and unique contributions to students’ psychological adjustment. Their research methods are limited by their reliance on retrospective measures and self-report measures of variables, and these results could be influenced by recall bias.

Two studies [ 29 , 64 ] considered the impact of how individuals view themselves on poor mental health. One study considered the impact of self-esteem and the association with non-accidental self-injury (NSSI) and suicide attempt amongst 734 university students. As rates of suicide and NSSI are higher amongst LGBT (lesbian, gay, bisexual, transgender) students, the prevalence of low self-esteem was compared. There was a low but statistically significant association between low self-esteem and NSSI, though not for suicide attempt. A large survey, including participants from seven universities [ 42 ] compared depressive symptoms in students with marked body image concerns, reporting that the risk of depressive symptoms was greater (OR 2.93) than for those with lower levels of body image concerns.

Mental health literacy and help seeking behaviour

Two studies [ 48 , 68 ] investigated attitudes to mental illness, mental health literacy and help seeking for mental health problems.

University students who lack sufficient mental health literacy skills to be able to recognise problems or where there are attitudes that foster shame at admitting to having mental health problems can result in students not recognising problems and/or failing to seek professional help [ 48 , 68 ]. Gorcyznski et al. (2017) [ 68 ] found that women and those who had a history of previous mental health problems exhibited significantly higher levels of mental health literacy. Greater mental health literacy was associated with an increased likelihood that individuals would seek help for mental health problems. They found that many students find it hard to identify symptoms of mental health problems and that 42% of students are unaware of where to access available resources. Of those who expressed an intention to seek help for mental health problems, most expressed a preference for online resources, and seeking help from family and friends, rather than medical professionals such as GPs.

Kotera et al. (2019) [ 48 ] identified self-compassion as an explanatory variable, reducing social comparison, promoting self-acceptance and recognition that discomfort is an inevitable human experience. The study found a strong, significant correlation between self-compassion and mental health symptoms ( r = -0.6. p < 0.01).

There again appears to be a cycle of reinforcement, where poor mental health symptoms are felt to be a source of shame and become hidden, help is not sought, and further isolation ensues, leading to further deterioration in mental health. Factors that can interrupt the cycle are self-compassion, leading to more readiness to seek help (see Fig. 2 ).

Poor mental health – cycles of reinforcement

Social networks

Nine studies [ 33 , 38 , 41 , 46 , 51 , 54 , 60 , 64 , 65 ] examined the concepts of loneliness and social support and its association with mental health in university students. One study also included students at other Higher Education Institutions [ 46 ]. Eight of the studies were surveys, and one was a retrospective case control study to examine the differences between university students and age-matched young people (non-university students) who attended hospital following deliberate self-harm [ 51 ].

Included studies demonstrated considerable variation in how they measured the concepts of social isolation, loneliness, social support and a sense of belonging. There were also differences in the types of outcomes measured to assess mental wellbeing and poor mental health. Grouping the studies within a broad category of ‘social factors’ therefore represents a limitation of this review given that different aspects of the phenomena may have been being measured. The tools used to measure these variables also differed. Only one scale (The UCLA loneliness scale) was used across multiple studies [ 41 , 60 , 65 ]. Diverse mental health outcomes were measured across the studies including positive affect, flourishing, self-harm, suicide risk, depression, anxiety and paranoia.

Three studies [ 41 , 60 , 62 ] measuring loneliness, two longitudinally [ 41 , 62 ], found a consistently positive association between loneliness and poor mental health in university students. Greater loneliness was linked to greater anxiety, stress, depression, poor general mental health, paranoia, alcohol abuse and eating disorder problems. The strength of the correlations ranged from 0–3-0.4 and were all statistically significant (see Tables 3 and 4 ). Loneliness was the strongest overall predictor of mental distress, of those measured. A strong identification with university friendship groups was most protective against distress relative to other social identities [ 60 ]. Whether poor mental health is the cause, or the result of loneliness was explored further in the studies. The results suggest that for general mental health, stress, depression and anxiety, loneliness induces or exacerbates symptoms of poor mental health over time [ 60 , 62 ]. The feedback cycle is evident, with loneliness leading to poor mental health which leads to withdrawal from social contacts and further exacerbation of loneliness.

Factors associated with protecting against loneliness by fostering supportive friendships and promoting mental wellbeing were also identified. Beliefs about the value of ‘leisure coping’, and attributes of resilience and emotional intelligence had a moderate, positive and significant association with developing mental wellbeing and were explored in three studies [ 46 , 54 , 66 ].

The transition to and first year at university represent critical times when friendships are developed. Thomas et al. (2020) [ 65 ] explored the factors that predict loneliness in the first year of university. A sense of community and higher levels of ‘social capital’ were significantly associated with lower levels of loneliness. ‘Social capital’ scales measure the development of emotionally supportive friendships and the ability to adjust to the disruption of old friendships as students transition to university. Students able to form close relationships within their first year at university are less likely to experience loneliness (r-0.09, r- 0.36, r- 0.34). One study [ 38 ] investigating the relationship between student experience and being the first in the family to attend university found that these students had lower ratings for peer group interactions.

Young adults at university and in higher education are facing multiple adjustments. Their ability to cope with these is influenced by many factors. Supportive friendships and a sense of belonging are factors that strengthen coping. Nightingale et al. (2012) undertook a longitudinal study to explore what factors were associated with university adjustment in a sample of first year students ( n = 331) [ 41 ]. They found that higher skills of emotion management and emotional self-efficacy were predictive of stable adjustment. These students also reported the lowest levels of loneliness and depression. This group had the skills to recognise their emotions and cope with stressors and were confident to access support. Students with poor emotion management and low levels of emotional self-efficacy may benefit from intervention to support the development of adaptive coping strategies and seeking support.

The positive and negative feedback loops

The relationship between the variables described appeared to work in positive and negative feedback loops with high levels of social capital easing the formation of a social network which acts as a critical buffer to stressors (see Fig. 3 ). Social networks and support give further strengthening and reinforcement, stimulating positive affect, engagement and flourishing. These, in turn, widen and deepen social networks for support and enhance a sense of wellbeing. Conversely young people who enter the transition to university/higher education with less social capital are less likely to identify with and locate a social network; isolation may follow, along with loneliness, anxiety, further withdrawal from contact with social networks and learning, and depression.

Triggers – factors that may act in combination with other factors to lead to poor mental health

Stress is seen as playing a key role in the development of poor mental health for students in higher education. Theoretical models and empirical studies have suggested that increases in stress are associated with decreases in student mental health [ 12 , 43 ]. Students at university experience the well-recognised stressors associated with academic study such as exams and course work. However, perhaps less well recognised are the processes of transition, requiring adapting to a new social and academic environment (Fisher 1994 cited by Denovan 2017a) [ 43 ]. Por et al. (2011) [ 46 ] in a small ( n = 130 prospective survey found a statistically significant correlation between higher levels of emotional intelligence and lower levels of perceived stress ( r = 0.40). Higher perceived stress was also associated with negative affect in two studies [ 43 , 46 ], and strongly negatively associated with positive affect (correlation -0.62) [ 54 ].

University variables

Eleven studies [ 35 , 39 , 47 , 51 , 52 , 54 , 60 , 63 , 65 , 83 , 84 ] explored university variables, and their association with mental health outcomes. The range of factors and their impact on mental health variables is limited, and there is little overlap. Knowledge gaps are shown by factors highlighted by our PPI group as potentially important but not identified in the literature (see Table 5 ). It should be noted that these may reflect the focus of our review, and our exclusion of intervention studies which may evaluate university factors.

High levels of perceived stress caused by exam and course work pressure was positively associated with poor mental health and lack of wellbeing [ 51 , 52 , 54 ]. Other potential stressors including financial anxieties and accommodation factors appeared to be less consistently associated with mental health outcomes [ 35 , 38 , 47 , 51 , 60 , 62 ]. Important mediators and buffers to these stressors are coping strategies and supportive networks (see conceptual model Appendix 2 ). One impact of financial pressures was that students who worked longer hours had less interaction with their peers, limiting the opportunities for these students to benefit from the protective effects of social support.

Red flags – behaviours associated with poor mental health and/or wellbeing

Engagement with learning and leisure activities.

Engagement with learning activities was strongly and positively associated with characteristics of adaptability [ 39 ] and also happiness and wellbeing [ 52 ] (see Fig. 4 ). Boulton et al. (2019) [ 52 ] undertook a longitudinal survey of undergraduate students at a campus-based university. They found that engagement and wellbeing varied during the term but were strongly correlated.

Engagement and wellbeing

Engagement occurred in a wide range of activities and behaviours. The authors suggest that the strong correlation between all forms of engagement with learning has possible instrumental value for the design of systems to monitor student engagement. Monitoring engagement might be used to identify changes in the behaviour of individuals to assist tutors in providing support and pastoral care. Students also were found to benefit from good induction activities provided by the university. Greater induction satisfaction was positively and strongly associated with a sense of community at university and with lower levels of loneliness [ 65 ].

The inte r- related nature of these variables is depicted in Fig. 4 . Greater adaptability is strongly associated with more positive engagement in learning and university life. More engagement is associated with higher mental wellbeing.

Denovan et al. (2017b) [ 54 ] explored leisure coping, its psychosocial functions and its relationship with mental wellbeing. An individual’s beliefs about the benefits of leisure activities to manage stress, facilitate the development of companionship and enhance mood were positively associated with flourishing and were negatively associated with perceived stress. Resilience was also measured. Resilience was strongly and positively associated with leisure coping beliefs and with indicators of mental wellbeing. The authors conclude that resilient individuals are more likely to use constructive means of coping (such as leisure coping) to proactively cultivate positive emotions which counteract the experience of stress and promote wellbeing. Leisure coping is predictive of positive affect which provides a strategy to reduce stress and sustain coping. The belief that friendships acquired through leisure provide social support is an example of leisure coping belief. Strong emotionally attached friendships that develop through participation in shared leisure pursuits are predictive of higher levels of well-being. Friendship bonds formed with fellow students at university are particularly important for maintaining mental health, and opportunities need to be developed and supported to ensure that meaningful social connections are made.

The ‘broaden-and-build theory’ (Fredickson 2004 [ 85 ] cited by [ 54 ]) may offer an explanation for the association seen between resilience, leisure coping and psychological wellbeing. The theory is based upon the role that positive and negative emotions have in shaping human adaptation. Positive emotions broaden thinking, enabling the individual to consider a range of ways of dealing with and adapting to their environment. Conversely, negative emotions narrow thinking and limit options for adapting. The former facilitates flourishing, facilitating future wellbeing. Resilient individuals are more likely to use constructive means of coping which generate positive emotion (Tugade & Fredrickson 2004 [ 86 ], cited by [ 54 ]). Positive emotions therefore lead to growth in coping resources, leading to greater well-being.

Health behaviours at university

Seven studies [ 29 , 31 , 38 , 45 , 51 , 54 , 66 ] examined how lifestyle behaviours might be linked with mental health outcomes. The studies looked at leisure activities [ 63 , 80 ], diet [ 29 ], alcohol use [ 29 , 31 , 38 , 51 ] and sleep [ 45 ].

Depressive symptoms were independently associated with problem drinking and possible alcohol dependence for both genders but were not associated with frequency of drinking and heavy episodic drinking. Students with higher levels of depressive symptoms reported significantly more problem drinking and possible alcohol dependence [ 31 ]. Mahadevan et al. (2010) [ 51 ] compared students and non-students seen in hospital for self-harm and found no difference in harmful use of alcohol and illicit drugs.

Poor sleep quality and increased consumption of unhealthy foods were also positively associated with depressive symptoms and perceived stress [ 29 ]. The correlation with dietary behaviours and poor mental health outcomes was low, but also confirmed by the negative correlation between less perceived stress and depressive symptoms and consumption of a healthier diet.

Physical activity and participation in leisure pursuits were both strongly correlated with mental wellbeing ( r = 0.4) [ 54 ], and negatively correlated with depressive symptoms and anxiety ( r = -0.6, -0.7) [ 66 ].

Thirty studies measuring the association between a wide range of factors and poor mental health and mental wellbeing in university and college students were identified and included in this review. Our purpose was to identify the factors that contribute to the growing prevalence of poor mental health amongst students in tertiary level education within the UK. We also aimed to identify factors that promote mental wellbeing and protect against deteriorating poor mental health.

Loneliness and social isolation were strongly associated with poor mental health and a sense of belonging and a strong support network were strongly associated with mental wellbeing and happiness. These associations were strongly positive in the eight studies that explored them and are consistent with other meta-analyses exploring the link between social support and mental health [ 87 ].

Another factor that appeared to be protective was older age when starting university. A wide range of personal traits and characteristics were also explored. Those associated with resilience, ability to adjust and better coping led to improved mental wellbeing. Better engagement appeared as an important mediator to potentially explain the relationship between these two variables. Engagement led to students being able to then tap into those features that are protective and promoting of mental wellbeing.

Other important risk factors for poor mental wellbeing that emerged were those students with existing or previous mental illness. Students on the autism spectrum and those with poor social problem-solving also were more likely to suffer from poor mental health. Negative self-image was also associated with poor mental health at university. Eating disorders were strongly associated with poor mental wellbeing and were found to be far more of a risk in students at university than in a comparative group of young people not in higher education. Other studies of university students also found that pre-existing poor mental health was a strong predictor of poor mental health in university students [ 88 ].

At a family level, the experience of childhood trauma and adverse experiences including, for example, neglect, household dysfunction or abuse, were strongly associated with poor mental health in young people at university. Students with a greater number of ‘adverse childhood experiences’ were at significantly greater risk of poor mental health than those students without experience of childhood trauma. This was also identified in a review of factors associated with depression and suicide related outcomes amongst university undergraduate students [ 88 ].

Our findings, in contrast to findings from other studies of university students, did not find that female gender associated with poor mental health and wellbeing, and it also found that being a mature student was protective of mental wellbeing.

Exam and course work pressure was associated with perceived stress and poor mental health. A lack of engagement with learning activities was also associated with poor mental health. A number of variables were not consistently shown to be associated with poor mental health including financial concerns and accommodation factors. Very little evidence related to university organisation or support structures was assessed in the evidence. One study found that a good induction programme had benefits for student mental wellbeing and may be a factor that enables students to become a part of a social network positive reinforcement cycle. Involvement in leisure activities was also found to be associated with improved coping strategies and better mental wellbeing. Students with poorer mental health tended to also eat in a less healthy manner, consume more harmful levels of alcohol, and experience poorer sleep.

This evidence review of the factors that influence mental health and wellbeing indicate areas where universities and higher education settings could develop and evaluate innovations in practice. These include:

Interventions before university to improve preparation of young people and their families for the transition to university.

Exploratory work to identify the acceptability and feasibility of identifying students at risk or who many be exhibiting indications of deteriorating mental health

Interventions that set out to foster a sense of belonging and identify

Creating environments that are helpful for building social networks

Improving mental health literacy and access to high quality support services

This review has a number of limitations. Most of the included studies were cross-sectional in design, with a small number being longitudinal ( n = 7), following students over a period of time to observe changes in the outcomes being measured. Two limitations of these sources of data is that they help to understand associations but do not reveal causality; secondly, we can only report the findings for those variables that were measured, and we therefore have to support causation in assuming these are the only factors that are related to mental health.

Furthermore, our approach has segregated and categorised variables in order to better understand the extent to which they impact mental health. This approach does not sufficiently explore or reveal the extent to which variables may compound one another, for example, feeling the stress of new ways of learning may not be a factor that influences mental health until it is combined with a sense of loneliness, anxiety about financial debt and a lack of parental support. We have used our PPI group and the development of vignettes of their experiences to seek to illustrate the compounding nature of the variables identified.

We limited our inclusion criteria to studies undertaken in the UK and published within the last decade (2009–2020), again meaning we may have limited our inclusion of relevant data. We also undertook single data extraction of data which may increase the risk of error in our data.

Understanding factors that influence students’ mental health and wellbeing offers the potential to find ways to identify strategies that enhance the students’ abilities to cope with the challenges of higher education. This review revealed a wide range of variables and the mechanisms that may explain how they impact upon mental wellbeing and increase the risk of poor mental health amongst students. It also identified a need for interventions that are implemented before young people make the transition to higher education. We both identified young people who are particularly vulnerable and the factors that arise that exacerbate poor mental health. We highlight that a sense of belonging and supportive networks are important buffers and that there are indicators including lack of engagement that may enable early intervention to provide targeted and appropriate support.

Availability of data and materials

Further details of the study and the findings can be provided on request to the lead author ([email protected]).

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Acknowledgements

We acknowledge the input from our public advisory group which included current and former students, and family members of students who have struggled with their mental health. The group gave us their extremely valuable insights to assist our understanding of the evidence.

This project was supported by funding from the National Institute for Health Research as part of the NIHR Public Health Research Programme (fuding reference 127659 Public Health Review Team). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

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All of the included authors designed the project methods and prepared a protocol. A.C. designed the search strategy. F.C, L.B and C.B screened the identified citations and undertook data extraction. S.B. led the PPI involvement. JD participated as a member of the PPI group. F.C and L.B undertook the analysis. F.C. and L.B wrote the main manuscript text. All authors reviewed the manuscript. F.C designed Figs. 2 , 3 and 4 . The author(s) read and approved the final manuscript.

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Campbell, F., Blank, L., Cantrell, A. et al. Factors that influence mental health of university and college students in the UK: a systematic review. BMC Public Health 22 , 1778 (2022). https://doi.org/10.1186/s12889-022-13943-x

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Student mental health
Mental wellbeing
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A systematic review: increasing mental health literacy in students through “The Guide”

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Published: 14 August 2024
Volume 4 , article number 96 , ( 2024 )

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Abouzar Nazari ORCID: orcid.org/0000-0003-2155-5438 1 ,
Gholamreza Garmaroudi ORCID: orcid.org/0000-0001-7449-227X 2 &
Marzie Rabiei ORCID: orcid.org/0009-0003-2297-9483 3

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Ensuring mental health literacy among students aged 10–25 is of utmost importance, and the efficacy of educational programs in this domain holds significant value. This systematic review assesses the influence of The Guide (Mental Health and High School Curriculum Guide) on mental health literacy within this demographic.

Materials and methods

This review examined how effective The Guide was in increasing students’ mental health literacy, help-seeking attitudes, and stigma reduction. It also looked at what factors influenced its implementation and sustainability in different settings. It followed the PRISMA guidelines and searched for studies that used The Guide or a modified version of it with students aged 10–25 from 1975 to 2023. Studies were assessed for quality using the QuADS Quality Appraisal tool.

Our systematic review encompassed a comprehensive analysis of 10 reports derived from five primary articles originating from six countries, with a combined participant pool of 4298 individuals. The selected studies exhibited variations in design, duration, delivery modes, and outcome measures. The synthesized findings underscored the positive impact of The Guide educational program on enhancing students' mental health literacy. However, the effects on students' help-seeking attitudes and stigma were varied. Additionally, the results illuminated that the success and sustainability of The Guide were contingent on several factors, including the mode of delivery, the role of facilitators or teachers, and the unique characteristics of the student population.

The review showed that The Guide was effective in improving students’ mental health literacy in different settings. It also suggested that The Guide needed to be adapted and tailored to the local context and culture, and that the facilitators or teachers and the students needed to be trained and involved in the process.

The Impact of Mental Health Literacy Training Programs on the Mental Health Literacy of University Students: a Systematic Review

Modifiable predictors of mental health literacy in the educational context: a systematic review and meta-analysis

A randomised controlled trial evaluating the Guide Cymru mental health literacy intervention programme in year 9 (age 13–14) school pupils in Wales

Avoid common mistakes on your manuscript.

1 Introduction

Mental health literacy (MHL), defined as the ability to recognize, understand, and respond to mental health problems, plays a pivotal role in promoting awareness, reducing stigma, and facilitating help-seeking behaviors [ 1 , 2 ]. Unlike traditional illness-based models, MHL interventions such as The Guide adopt a population-based approach, focusing on equipping individuals with knowledge and skills pertinent to mental health across diverse contexts [ 3 ].

Children and youth represent a critical target group for MHL interventions, given their susceptibility to mental health challenges and potential barriers to accessing appropriate care [ 4 , 5 ]. Globally, a substantial proportion of young people experience mental health disorders, yet access to professional support remains limited [ 4 , 5 , 6 , 7 , 8 ]. Enhancing MHL among youth empowers them to identify mental health issues, seek timely assistance, and dispel misconceptions that perpetuate stigma [ 9 ].

Stigma, characterized by labeling, devaluation, and discrimination based on perceived differences, profoundly impacts young people’s mental outcomes. It hinders help-seeking behaviors and diminishes self-esteem, exacerbating the challenges of managing mental health issues [ 10 ]. Effective MHL interventions, such as The Guide, have the potential to mitigate stigma by promoting accurate understanding, positive attitudes, and empathetic responses toward mental health concerns [ 1 , 11 ].

Educational interventions represent a cornerstone in delivering MHL education to youth, aiming to cultivate informed attitudes and encourage proactive help-seeking behaviors [ 12 , 13 ]. The effectiveness of these interventions, however, varies based on content, delivery methods, and evaluation frameworks [ 14 ]. The Guide, an educational program derived from the Mental Health and High School Curriculum Guide (MHHSCG) [ 15 ], is specifically designed to meet these objectives within educational settings. Notably, while rooted in educational contexts, The Guide's adaptable format allows for potential implementation in diverse environments such as workplaces and communities, albeit with necessary adjustments to suit specific needs and dynamics.

This systematic review aims to assess the impact of The Guide educational program on enhancing MHL among individuals aged 10–25 across various settings. Specifically, this review seeks to: (1) identify and evaluate the quality of studies examining The Guide's effectiveness in improving MHL; (2) synthesize findings regarding MHL outcomes including knowledge, stigma, and help-seeking attitudes; (3) explore factors influencing The Guide's implementation and sustainability in different settings; and (4) discuss implications for research, policy, and practical applications.

2.1 Search strategy

This systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews) guidelines, employed a comprehensive search strategy to identify relevant studies. The search was conducted in February 2023, covering five key databases: PubMed, Web of Science, Scopus, Cochrane Library, and Embase. The inclusion criteria were English language articles published between 2009 and 2023. To ensure a thorough examination of the existing literature, reference lists of all included studies and relevant review articles were meticulously scrutinized. Unpublished data, grey literature (including dissertations, congress abstracts, and patents), and duplicate citations were excluded from this systematic review. Each database received a tailored search string, adapted to its unique requirements. Supplementary material 1 provides an example of the search string used in PubMed. Additionally, combined keyword searching was implemented with the following terms: ‘Mental health literacy OR (Mental health AND literacy)’ AND ‘Student* OR Adolescen* OR Youth OR Pupil OR Teen* OR School* OR Child*’ AND ‘Clinical Trial OR Randomized controlled trial OR Non-Randomized Controlled Trials OR Controlled Clinical Trial OR randomized OR randomly OR pre posttest study OR quasi-experimental’. Notably, Google Scholar, along with selected reference lists, was also explored to identify additional studies of interest beyond the initial database searches. It’s crucial to highlight that Google Scholar searches complemented academic database searches, contributing to a more comprehensive retrieval of relevant literature. This systematic review is registered on PROSPERO under the registration number CRD42023314882.

2.2 Eligibility criteria

2.2.1 inclusion criteria.

Studies eligible for inclusion addressed individuals aged 10–25 years old, employing study designs such as randomized controlled trials, non-randomized controlled trials, experimental, or before-and-after studies. Included studies delivered the intervention program of The Guide or its modified versions. Additionally, studies were required to have a control group or provide an intervention as treatment as usual. Eligible studies assessed help-seeking intentions/attitudes, mental health-related stigma, and/or mental health literacy directly through the self-report of young people. Language inclusion criteria stipulated that studies must be published in English.

2.2.2 Exclusion criteria

Conversely, studies were excluded if they lacked information about participants' age. Additionally, studies with observational designs or those without random allocation were not considered. Interventions other than The Guide or studies with no intervention were excluded. Lack of a control group led to exclusion, as did outcomes assessed from caregivers or teachers, or those measuring dimensions other than help-seeking intentions/attitudes, mental health-related stigma, or mental health literacy. Lastly, studies published in languages other than English were excluded. This comprehensive set of criteria ensured a meticulous selection process, aiming for a focused and relevant body of evidence in the systematic review.

2.2.3 Selection of studies

The titles and abstracts of the retrieved records were screened by two independent reviewers (A.N. and M.R.) using the eligibility criteria. The full texts of the potentially eligible records were obtained and assessed by the same reviewers. Any disagreements were resolved by discussion or consultation with a third reviewer (GH.G.). A PRISMA flow diagram was used to report the study selection process.

2.2.4 Data extraction

The data extracted from the included studies are summarized in Table 1 . The data extracted included: first author’s name, publication year, study population, sample size, participants’ sex, number of subjects in each group, age range and average age of participants, trial design, type of intervention, and Posttest time.

2.2.5 Risk of bias assessment

The assessment of the risk of bias was conducted using the Quality Assessment and Developmental Evaluation (QuADS) tool [ 16 ] which is designed to appraise the quality and risk of bias in systematic reviews of mixed- or multi-method studies. The tool comprises eight aspects, each scored from 0 to 3, with the total score indicating the overall quality level of the study. Studies were categorized as excellent (above 80%), good (between 50 and 80%), or low (below 50%) based on their total scores. The risk of bias assessment was conducted by the review team, including A.N. and M.R., with a focus on various aspects such as research aims, settings, populations, designs, analytic methods, and the consideration of research stakeholders’ perspectives. The risk of bias assessment was considered in the interpretation of findings, ensuring that the conclusions drawn accounted for the quality and reliability of the evidence. Studies with a high risk of bias were noted, and their findings were interpreted with caution, particularly in terms of their contribution to overall conclusions.

2.2.6 Data synthesis

Narrative synthesis was utilized to analyze and interpret the results derived from the studies included in this review. This approach entailed qualitatively summarizing the findings, identifying patterns, variations, and relationships across the studies. The synthesis specifically delved into examining the impact of The Guide on outcomes such as mental health literacy, help-seeking attitudes, and mental health-related stigma. Given that no meta-analysis or moderator analysis was conducted in the review, the synthesis predominantly relied on a qualitative narrative approach to offer a comprehensive overview of the evidence. Any discerned patterns or trends were discussed within the framework of the study objectives, elucidating their implications for research, policy, and practice.

2.2.7 Intervention description

The Guide, developed in 2009 as an adaptation of the Mental Health & High School Curriculum Guide (MHHSCG), is a modular web-based resource designed to enhance mental health literacy among youth aged 10–25 [ 14 , 15 ]. It consists of six core modules covering topics such as understanding mental health, recognizing symptoms, seeking help, and reducing stigma. Each module incorporates interactive activities, case studies, and resources tailored to educational contexts, facilitating engagement and knowledge retention among users.

The literature search yielded a total of 1234 records, of which 462 were duplicates and were removed. 734 were excluded after screening the titles and abstracts. The full texts of the remaining 42 studies were assessed for eligibility, and 5 studies [ 17 , 18 , 19 , 20 , 21 ] met the inclusion criteria and it's noteworthy that one of the articles included two reports due to the study being conducted in two different countries and reported separately in one article. The main reasons for exclusion were: not using The Guide as the intervention, not having a control group, and outside the age range of 10–25. The PRISMA flow diagram [ 22 ] of the study selection process is shown in Fig. 1 .

PRISMA 2020 flow diagram updated of papers included in the review Improving mental health literacy in students aged 10–25 with The Guide educational program: A systematic review, search period: 1975 to February 2023

A new literature search was conducted to update the previous review and identify any new studies that evaluated the effectiveness of The Guide. The new search resulted in 130 records from various databases and sources. After removing 35 duplicates, 60 records were excluded based on the screening of titles and abstracts. The remaining 42 full-text records were assessed for eligibility using the same criteria as the previous review. Only 2 studies [ 23 , 24 ] met all the criteria and were added to the review. The reasons for exclusion were the same as the previous review.

The characteristics, quality, and risk of bias of the included studies are summarized in Table 1 and Supplementary material 2. The studies were conducted in eight different countries: Iran, Ethiopia, Vietnam, Cambodia, Nicaragua, Canada, Germany, and Wales. The study population comprised students aged 10–25 from various educational settings, including schools, colleges, universities, and community organizations. The total sample size across all studies was 7420 participants, with a proportion of female participants ranging from 50 to 100%. The mean age of participants across studies was 15.93 years.

The study design was either randomized controlled trial (RCT), quasi-experimental design, or pre-post evaluation. The intervention was The Guide or a modified version of it. The duration of the intervention ranged from 2 to 12 weeks, with a mean of 8.14 weeks. The total time of the intervention ranged from 5 to 14 h, with a mean of 10.16 h (One of the studies reported the duration of the intervention as 1 day). The intervention was delivered either online or in-person by trained facilitators or teachers.

The outcome measures were validated instruments or scales for mental health literacy or help-seeking attitudes and stigma, such as the Mental Health Literacy Scale (MHL), the Mental Health Knowledge Schedule (MHK), the Mental Health Knowledge and Attitude Scale (MHKAS), the Attitudes Towards Seeking Professional Psychological Help Scale (ATMI), the Mental health knowledge (MHK), the Knowledge and Attitudes to Mental Health Scales (KAMHS) or the Mental Health Knowledge and Awareness Assessment (MHKAA). The Posttest time ranged from immediately after the intervention to 6 months later.

The quality and risk of bias of the included studies were assessed using the QuADS Quality Appraisal [ 16 ]. The overall quality and risk of bias of the studies were moderate to high, with some concerns in domains such as randomization, allocation concealment, blinding, attrition, measurement, reporting, and confounding. The study by Simkiss [ 23 ] in Wales had the highest quality score (97%), while the study by Zare [ 17 ] in Iran had the lowest quality score (61%). A detailed breakdown of these assessments is provided in Supplementary Material 2.

The data from the included studies were synthesized using a narrative approach. The main findings are reported below according to the research questions and hypotheses.

The Table 2 shows the mean change and standard deviation of the outcome measures for the intervention and control groups in six studies. The outcome measures were mental health knowledge, attitudes, literacy, or stigma. The intervention group received The Guide or a modified version of it, while the control group received no intervention, usual care, or another intervention. The table also shows the p-value of the difference between the intervention and control groups, which indicates the statistical significance of the difference.

3.1 Effectiveness of The Guide in improving mental health literacy

Zare [ 17 ] measured it using a self-developed questionnaire and found that the intervention group had a mean change of 54.08 (7.70), while the control group had a mean change of 1.28 (6.09). The difference was statistically significant (p < 0.001). Hassen [ 18 ] measured mental health literacy using the Mental Health Literacy Questionnaire and found that the intervention group had a mean change of 27.41 (19.55), while the control group had a mean change of 20.98 (16.54). The difference was statistically significant (p < 0.05). This result suggests that The Guide improved students’ mental health literacy more than the control group, which received no intervention or usual care.

Nguyen [ 19 ] measured it using the MHL-Knowledge scale and found that the intervention group had a mean change of 0.06 (0.11) in Vietnam and 0.04 (0.09) in Cambodia, while the control group had a mean change of 0.01 (0.07) in Vietnam and − 0.02 (0.08) in Cambodia. The difference was statistically significant in both countries (p < 0.05). Ravindran [ 20 ] measured it using the MHKAS-Knowledge scale and found that the intervention group had a mean change of 2.23 (2.84), while the control group had a mean change of − 1.43 (4.52). The difference was statistically significant (p < 0.001). Milin [ 21 ] measured it using a self-developed scale and found that the intervention group had a mean change of 0.7 (1.47), while the control group had a mean change of − 0.18 (1.48). The difference was statistically significant (p < 0.001). These results suggest that The Guide improved students’ mental health literacy compared to no intervention or usual care.

Simkiss [ 23 ] measured mental health literacy using the Knowledge and Attitudes about Mental Health Scale (KAMHS) and found that the intervention group had a mean change of 0.09 (0.09), while the control group had a mean change of − 0.01 (0.09). The difference was statistically significant (p < 0.05). This result suggests that The Guide improved students’ mental health literacy more than the control group, which received usual care. Freţian [ 24 ] conducted a quasi-experimental pre-post study in Germany with students aged 14–17. The intervention involved The Guide and the Mental Health Knowledge (MHK) scale over a single day. The intervention group displayed a statistically significant mean change in mental health knowledge (p < 0.05).

3.2 Effectiveness of The Guide in improving help-seeking attitudes and stigma

Nguyen [ 19 ] measured them using the Stigma scale and found that the intervention group had a mean change of − 0.09 (0.37) in Vietnam and − 0.66 (0.50) in Cambodia, while the control group had a mean change of − 0.03 (0.39) in Vietnam and − 0.08 (0.52) in Cambodia. The difference was not statistically significant in both countries (p > 0.05). Ravindran [ 20 ] measured them using the MHKAS-Attitudes scale and found that the intervention group had a mean change of 0.54 (1.87), while the control group had a mean change of − 0.28 (1.79). The difference was statistically significant (p < 0.001). Milin [ 21 ] measured them using the Attitudes Towards Mental Illness scale and found that the intervention group had a mean change of 1.3 (2.46), while the control group had a mean change of − 1.17 (2.63). The difference was statistically significant (p < 0.001). These results suggest that The Guide had a mixed effect on improving students’ attitudes and reducing their stigma compared to no intervention or usual care.

3.3 Factors influencing the implementation and sustainability of The Guide

Some studies reported on the factors that influenced the implementation and sustainability of The Guide in different contexts. These factors included:

Mode of delivery The Guide was delivered either online or in-person by trained facilitators or teachers. The mode of delivery affected the accessibility, engagement, and interaction of the students with the program. For example, Nguyen [ 19 ] found that online delivery was more convenient and flexible for the students, but also posed some challenges such as technical issues, low attendance, and limited feedback. Milin [ 21 ] found that online delivery was more effective than in-person delivery in improving mental health knowledge and attitudes, but also required more support and guidance from the teachers. Simkiss [ 23 ] found that online delivery was more acceptable and feasible for the students and the schools, but also needed more resources and infrastructure to ensure the quality and fidelity of the program.

Duration and frequency of the sessions The total number of sessions varied across interventions in different studies, ranging from 2 to 12 weeks (with a mean duration of 7.1 weeks) and from 1 to 14 h (with a mean of 8.9 h). The duration and frequency of the sessions affected the retention, completion, and satisfaction of the students with the program. For example, Zare [ 17 ] found that a shorter duration (6 weeks) and a longer time (9 h) of the sessions resulted in a higher retention rate (95%) and a higher completion rate (90%) than a longer duration (12 weeks) and a shorter time (6 h) of the sessions in another study. Ravindran [ 20 ] found that a longer duration (6 weeks) and a longer time (12 h) of the sessions resulted in a higher satisfaction rate (90%) than a shorter duration (5 weeks) and a shorter time (7.5 h) of the sessions in another study. Freţian [ 24 ] found that a shorter duration (2 weeks) and a shorter time (1 h) of the sessions resulted in a lower dropout rate (5%) and a higher satisfaction rate (95%) than a longer duration (6 weeks) and a longer time (6 h) of the sessions in another study.

Characteristics of the facilitators or teachers The facilitators or teachers who delivered The Guide were either trained professionals or peers who had received training on the program content and methods. The characteristics of the facilitators or teachers affected the quality, fidelity, and effectiveness of the program delivery. For example, Hassen [ 18 ] found that peer educators were more relatable, credible, and engaging for the students than professionals, but also faced some challenges such as lack of confidence, experience, and supervision. Milin [ 21 ] found that teachers who delivered The Guide had more positive attitudes towards mental health than those who did not, but also needed more training and support to deliver the program effectively. Simkiss [ 23 ] found that facilitators who delivered The Guide had more knowledge and skills in mental health than those who did not, but also required more monitoring and feedback to ensure the consistency and quality of the program.

Characteristics of the students The students who participated in The Guide were aged 10–25 from various educational settings and cultural backgrounds. The diverse characteristics of students played a significant role in shaping their engagement with the program, influencing motivation, participation, and subsequent learning outcomes. Nguyen [ 19 ] highlighted this by illustrating that students from Vietnam and Cambodia exhibited varying levels of mental health knowledge, attitudes, and stigma both before and after the program. These differences were closely tied to the distinct cultural values and beliefs surrounding mental health in each region. Milin [ 21 ] found that students who had higher levels of mental health knowledge and lower levels of stigma before the program benefited more from the program than those who had lower levels of knowledge and higher levels of stigma. Freţian [ 24 ] found that students who had lower levels of mental health knowledge and higher levels of stigma before the program showed more improvement in their knowledge and attitudes than those who had higher levels of knowledge and lower levels of stigma.

4 Discussion

The primary objective of this systematic review was to assess the effectiveness of The Guide, a school-based program aimed at enhancing mental health literacy, improving attitudes, and reducing stigma among students aged 10–25. The study followed the PRISMA guidelines and examined ten studies from eight countries, encompassing a diverse range of participants and methodologies.

The review found that The Guide had a positive impact on mental health literacy and a mixed impact on help-seeking attitudes and stigma compared to no intervention or usual care [ 25 ]. The magnitude of the impact varied from small to large, depending on the instrument or scale used and the post-test time. These findings are consistent with previous reviews that have shown that mental health literacy interventions can improve mental health knowledge, attitudes, and behaviors among young people [ 26 , 27 , 28 ]. Mental health literacy is considered a key strategy to facilitate early intervention and prevention of mental disorders, as well as to promote mental health and well-being among young people [ 9 , 29 ]. By improving mental health literacy, young people can enhance their awareness, understanding, and skills related to mental health, reduce stigma and discrimination towards people with mental disorders, and increase their help-seeking efficacy and use of appropriate services [ 9 , 29 , 30 ]. The overall impact of The Guide on mental health literacy outcomes across all studies was moderate, indicating a noticeable improvement in mental health literacy after participating in The Guide.

The review also found that The Guide had a mixed impact on help-seeking attitudes and stigma among students aged 10–25. Help-seeking attitudes, representing the willingness to seek help for mental issues, and stigma, encompassing negative stereotypes and prejudices, are interconnected. The results of this review indicate that The Guide can improve help-seeking attitudes and reduce stigma among some groups of students, but not among others [ 19 , 20 , 21 ]. The overall impact of The Guide on help-seeking attitudes and stigma outcomes across all studies was small, indicating a slight improvement in help-seeking attitudes and a slight reduction in stigma after participating in The Guide. Research exploring why these outcomes vary among students is limited but crucial. Potential factors include cultural and contextual differences influencing perceptions of mental health interventions [ 31 , 32 , 33 ], perceived barriers to seeking help such as stigma and confidentiality concerns [ 34 , 35 ], variability in program implementation quality [ 31 ], and individual differences in developmental stages and personal experiences with mental health [ 31 , 34 ]. These factors suggest the need for further qualitative exploration of students’ perspectives and experiences to optimize the effectiveness of future MHL interventions like The Guide in promoting mental health awareness and reducing stigma among youth.

However, the review also found that the impact of The Guide may vary depending on the context, culture, and characteristics of the students and the facilitators or teachers. The review identified four main factors that influenced the implementation and sustainability of The Guide in different contexts: mode of delivery, duration and frequency of the sessions, characteristics of the facilitators or teachers, and characteristics of the students [ 31 , 32 , 33 , 34 ]. These factors affected the accessibility, engagement, interaction, retention, completion, satisfaction, quality, fidelity, and effectiveness of the program delivery [ 35 ]. The review suggested that The Guide needed to be adapted and tailored to the local context and culture, and that the facilitators or teachers and the students needed to be trained and involved in the planning, delivery, and evaluation of The Guide.

4.1 Limitations

The review has some limitations that should be acknowledged. First, the quality and risk of bias of the included studies were variable, which may affect the validity and reliability of the findings. Second, the heterogeneity of the studies in terms of population, intervention, comparison, outcome, and study design limited the possibility of conducting a meta-analysis or a subgroup analysis. Third, the review only included studies published in English, which may exclude relevant studies published in other languages. Fourth, the review only focused on The Guide or a modified version of it as an intervention, which may not capture other types of mental health literacy interventions that may be effective for young people.

4.2 Implications for practice and research

The systematic review underscores several implications for both practice and research. For practice, The Guide emerges as a valuable tool for advancing mental health education and awareness among young people, while also fostering reductions in stigma and improvements in help-seeking behavior. However, successful adoption and implementation of The Guide necessitate careful consideration of potential barriers and facilitators, including resource availability, participant readiness, alignment with local cultural norms, and robust evaluation mechanisms. Moreover, adequate training and ongoing support for facilitators and teachers are crucial, alongside active engagement of students and stakeholders in all stages of planning, delivery, and evaluation. In terms of research, there is a clear need for more high-quality studies to assess the long-term effects of The Guide on mental health outcomes across diverse demographic backgrounds and settings. Furthermore, future research should explore effective strategies for integrating The Guide into existing educational curricula or policies to ensure sustainability and scalability. Methodologically, there is a call for studies that delve deeper into the mechanisms and moderators influencing The Guide's impact on various mental health outcomes, utilizing more rigorous and standardized methods and measures. Additionally, comparative research that contrasts The Guide with other mental health literacy interventions or control conditions would provide valuable insights into its unique contributions.

5 Conclusion

The systematic review of The Guide, a mental health intervention targeting young people aged 10–25, underscores its positive impact on enhancing mental health literacy and help-seeking attitudes, though effect sizes vary across studies. Factors influencing its implementation and sustainability include delivery mode, session frequency, facilitator characteristics, and student demographics. While this review contributes valuable insights, limitations such as variability in study quality, number, and data heterogeneity highlight the necessity for more robust research efforts. Implications for practice include recognizing The Guide's potential across diverse educational and cultural settings, contingent upon addressing implementation barriers and optimizing facilitator training. Future research should prioritize enhancing measurement validity, exploring long-term effects, and assessing implementation costs to advance both understanding and application of The Guide. Expanding on the potential long-term impacts of The Guide and its scalability in various educational contexts could enrich the conclusion, offering a forward-looking perspective on its sustained effectiveness and broader implications for mental health interventions among young populations.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. No datasets were generated or analysed during the current study.

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Abouzar Nazari

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Nazari, A., Garmaroudi, G. & Rabiei, M. A systematic review: increasing mental health literacy in students through “The Guide”. Discov Psychol 4 , 96 (2024). https://doi.org/10.1007/s44202-024-00219-1

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Published: 22 June 2023

Social media and mental health in students: a cross-sectional study during the Covid-19 pandemic

Abouzar Nazari ORCID: orcid.org/0000-0003-2155-5438 1 ,
Maede Hosseinnia ORCID: orcid.org/0000-0002-2248-7011 2 ,
Samaneh Torkian 3 &
Gholamreza Garmaroudi ORCID: orcid.org/0000-0001-7449-227X 4

BMC Psychiatry volume 23 , Article number: 458 ( 2023 ) Cite this article

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Social media causes increased use and problems due to their attractions. Hence, it can affect mental health, especially in students. The present study was conducted with the aim of determining the relationship between the use of social media and the mental health of students.

Materials and methods

The current cross-sectional study was conducted in 2021 on 781 university students in Lorestan province, who were selected by the Convenience Sampling method. The data was collected using a questionnaire on demographic characteristics, social media, problematic use of social media, and mental health (DASS-21). Data were analyzed in SPSS-26 software.

Shows that marital status, major, and household income are significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Also, problematic use of social media (β = 3.54, 95% CI: (3.23, 3.85)) was significantly associated with higher mental health scores (a higher DASS21 score means worse mental health status). Income and social media use (β = 1.02, 95% CI: 0.78, 1.25) were significantly associated with higher DASS21 scores (a higher DASS21 score means worse mental health status). Major was significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status).

This study indicated that social media had a direct relationship with mental health. Despite the large amount of evidence suggesting that social media harms mental health, more research is still necessary to determine the cause and how social media can be used without harmful effects.

Peer Review reports

Social media

Social media is one of the newest and most popular internet services, which has caused significant progress in the social systems of different countries in recent years [ 1 , 2 ]. The use of the Internet has become popular among people in such a way that its use has become inevitable and has made life difficult for those who use it excessively [ 3 ]. Social media has attracted the attention of millions of users around the world owing to the possibility of fast communication, access to a large amount of information, and its widespread dissemination [ 4 ]. Facebook, WhatsApp, Instagram, and Twitter are the most popular media that have attractive and diverse spaces for online communication among users, especially the young generation [ 5 , 6 ].

According to studies, at least 55% of the world’s population used social media in 2022 [ 7 ]. Iranian statistics also indicate that 78.5% of people use at least one social media. WhatsApp, with 71.1% of users, Instagram, with 49.4%, and Telegram, with 31.6% are the most popular social media among Iranians [ 8 , 9 ].

The use of social media has increased significantly in all age groups due to the origin of the COVID-19 pandemic [ 10 ] .It affected younger people, especially students, due to educational and other purposes [ 11 , 12 ]. Because of the sudden onset of the COVID-19 pandemic, educational institutions and learners had to accept e-learning as the only sustainable education option [ 13 ]. The rapid migration to E-learning has brought several challenges that can have both positive and negative consequences [ 14 ].

Unlike traditional media, where users are passive, social media enables people to create and share content; hence, they have become popular tools for social interaction [ 15 ].The freedom to choose to participate in the company of friends, anonymity, moderation, encouragement, the free exchange of feelings, and network interactions without physical presence and the constraints of the real world are some of the most significant factors that influence users’ continued activity in social media [ 16 ]. In social media, people can interact, maintain relationships, make new friends, and find out more about the people they know offline [ 17 ]. However, this popularity has resulted in significant lifestyle changes, as well as intentional or unintentional changes in various aspects of human social life [ 18 ]. Despite many advantages, the high use of social media brings negative physical, psychological, and social problems and consequences [ 19 ], but despite the use and access of more people to the Internet, its consequences and crises have been ignored [ 20 ].

Use of social media and mental health

Spending too much time on social media can easily become problematic [ 21 ]. Excessive use of social media, called problematic use, has symptoms similar to addiction [ 22 , 23 ]. Problematic use of social media represents a non-drug-related disorder in which harmful effects emerge due to preoccupation and compulsion to over-participate in social media platforms despite its highly negative consequences [ 24 , 25 , 26 ], which leads to adverse consequences of mental health, including anxiety, depression, lower well-being, and lower self-esteem [ 27 , 28 , 29 ].

Mental health & use of social media

Mental health is the main pillar of healthy human societies, which plays a vital role in ensuring the dynamism and efficiency of any society in such a way that other parts of health cannot be achieved without mental health [ 30 ]. According to World Health Organization’s (WHO) definition, mental health refers to a person’s ability to communicate with others [ 31 ]. Some researchers believe that social relationships can significantly affect mental health and improve quality of life by creating a sense of belonging and social identity [ 32 ]. It is also reported that people with higher social interactions have higher physical and mental health [ 33 ].

Scientific evidence also shows that social media affect people’s mental health [ 34 ]. Social studies and critiques often emphasize the investigation of the negative effects of Internet use [ 35 ]. For example, Kim et al. studied 1573 participants aged 18–64 years and reported that Internet addiction and social media use were associated with higher levels of depression and suicidal thoughts [ 36 ]. Zadar also studied adults and reported that excessive use of social media and the Internet was correlated with stress, sleep disturbances, and personality disorders [ 37 ]. Richards et al. reported the negative effects of the Internet and social media on the health and quality of life of adolescents [ 38 ]. There have been numerous studies that examine Internet addiction and its associated problems in young people [ 39 , 40 ], as well as reports of the effects of social media use on young people’s mental health [ 41 , 42 ].

A study on Iranian students showed that social media leads to depression, anxiety, and mental health decline [ 25 ]. A study on Iranian students showed that social media leads to depression, anxiety, and mental health decline [ 25 ]. But no study has investigated the effects of social media on the mental health of students from a more traditional province with lower individualism and higher levels of social support (where they were thought to have lower social media use and better mental health) during the COVID-19 pandemic. As social media became more and more vital to university students’ social lives during the lockdowns, students were likely at increased risk of social media addiction, which could harm their mental health. University students depended more on social media due to the limitations of face-to-face interactions. In addition, previous studies were conducted exclusively on students in specific fields. However, in our study, all fields, including medical and non-medical science fields were investigated.

The present study was conducted to determine the relationship between the use of social media and mental health in students in Lorestan Province during the COVID-19 pandemic.

Study design and participants

The current study was descriptive-analytical, cross-sectional, and conducted from February to March 2022 with a statistical population made up of students in all academic grades at universities in Lorestan Province (19 scientific and academic centers, including centers under the supervision of the Ministry of Health and the Ministry of Science).

Sample size

According to the convenience sampling method, 781 people were chosen as participants in the present study. During the sampling, a questionnaire was created and uploaded virtually on Porsline’s website, and then the questionnaire link was shared in educational and academic groups on social media for students to complete the questionnaire under inclusion criteria (being a student at the University of Lorestan and consenting to participate in the study).

The research tools included the demographic information questionnaire, the standard social media use questionnaire, and the mental health questionnaire.

Demographic information

The demographic information age, gender, ethnicity, province of residence, urban or rural, place of residence, semester, and the field of study, marital status, household income, education level, and employment status were recorded.

Psychological assessment

The students were subjected to the Persian version of the Depression Anxiety Stress Scale (DASS21). It consists of three self-report scales designed to measure different emotional states. DASS21 questions were adjusted according to their importance and the culture of Iranian students. The DASS21 scale was scored on a four-point scale to assess the extent to which participants experienced each condition over the past few weeks. The scoring method was such that each question was scored from 0 (never) to 3 (very high). Samani (2008) found that the questionnaire has a validity of 0.77 and a Cronbach’s alpha of 0.82 [ 43 ].

Use of social media questionnaire

Among the 13 questions on social media use in the questionnaire, seven were asked on a Likert scale (never, sometimes, often, almost, and always) that examined the problematic use of social media, and six were asked about how much time users spend on social media. Because some items were related to the type of social media platform, which is not available today, and users now use newer social media platforms such as WhatsApp and Instagram, the questionnaires were modified by experts and fundamentally changed, and a 22-item questionnaire was obtained that covered the frequency of using social media. Cronbach’s alpha was equal to 0.705 for the first part, 0.794 for the second part, and 0.830 for all questions [ 44 ]. Considering the importance of the problematic use of the social media, six questions about the problematic use were measured separately.

To confirm the validity of the questionnaire, a panel of experts with CVR 0.49 and CVI 0.70 was used. Its reliability was also obtained (0.784) using Cronbach’s alpha coefficient. Finally, the questionnaire was tested in a class with 30 students to check the level of difficulty and comprehension of the questionnaire. Finally, a 22-item questionnaire was obtained, of which six items were about the problematic use of social media and the remaining 16 questions were about the rate and frequency of using social media. Cronbach’s alpha was 0.705 for the first part, including questions about the problematic use of the social media, and 0.794 for the second part, including questions about the rate and frequency of using the social media. The total Cronbach’s alpha for all questions was 0.830. Six questions about the problematic use of social media were measured separately due to the importance of the problematic use of social media. Also, a separate score was considered for each question. The scores of these six questions on the problematic use of the social media were summed, and a single score was obtained for analysis.

Statistical analysis

Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 26.0 (SPSS Inc., Chicago, IL, USA). The normal distribution of continuous variables was analyzed using the Kolmogorov-Smirnov test, histogram, and P-P diagram, which showed that they are not normally distributed. Descriptive statistics were calculated for all variables. Comparison between groups was done using Mann-Whitney and Kruskal-Wallis non-parametric tests. Multiple linear regression analysis was used to investigate the relationship between mental health, problematic use of social media, and social media use (The result of merging the Frequency of using social media and Time to use social media). Generalized Linear Models (GLM) were used to assess the association between mental health with the use of social media and problematic use of social media. Due to the high correlation (r = 0.585, p = < 0.001) between the use of social media and problematic use of social media, collinearity, we run two separate GLM models. Regression coefficients (β) and adjusted β (β*) with 95% CI and P-value were reported.

A total of 781 participants completed the questionnaires, of which 64.4% were women and 71.3% were single. The minimum age of the participants was 17 years, the maximum age was 45 years, and about half of them (48.9%) were between 21 and 25 years old. A total of 53.4% of the participants had bachelor’s degrees. The income level of 23.2% of participants was less than five million Tomans (the currency of Iran), and 69.7% of the participants were unemployed. 88.1% were living with their families and 70.8% were studying in non-medical fields. 86% of the participants lived in the city, and 58.9% were in their fourth semester or higher. Considering that the research was conducted in a Lorish Province, 43.8% of participants were from the Lorish ethnicity.

The mean total score of mental health was 12.30 with a standard deviation of 30.38, and the mean total score of social media was 14.5557 with a standard deviation of 7.74140.

Table 1 presents a comparison of the mean problematic use of social media and mental health with demographic variables. Considering the non-normality of the hypothesis H0, to compare the means of the independent variables, Mann-Whitney non-parametric tests (for the variables of gender, the field of study, academic semester, employment status, province of residence, and whether it is rural or urban) and Kruskal Wallis (for the variables age, ethnicity, level of education, household income and marital status). According to the obtained results, it was found that the score of problematic use of social media is significantly higher in women, the age group less than 20 years, unemployed, non-native students, dormitory students, and students living with friends or alone, Fars students, students with a household income level of fewer than 7 million Tomans(Iranian currency), and single, divorced, and widowed students were higher than the other groups(P < 0.05).

By comparing the mean score of mental health with demographic variables using non-parametric Mann-Whitney and Kruskal Wallis tests, it was found that there is a significant difference between the variable of poor mental health and all demographic variables (except for the semester variable), residence status (rural or urban) and education level. (There was a significant relationship (P < 0.05). In such a way that the mental health condition was worse in women, age group less than 20 years old, non-medical science, unemployed, non-native, and dormitory students. Also, Fars students, divorced, widowed, and students with a household income of fewer than 5 million Tomans (Iranian currency) showed poorer mental health status. (Table 1 ).

The final model shows that marital status, field, and household income were significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Being single (β* = -23.03, 95% CI: (-33.10, -12.96), being married (β* = -38.78, 95% CI: -51.23, -26.33), was in Medical sciences fields (β* = -8.15, 95% CI: -11.37, -4.94), and have income 7–10 million (β* = -5.66, 95% CI: -9.62, -1.71) were significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Problematic use of social media (β* = 3.54, 95% CI: (3.23, 3.85) was significantly associated with higher mental health scores (a higher DASS21 score means worse mental health status). (Table 2 )

Age, income, and use of social media (β* = 1.02, 95% CI: 0.78, 1.25) were significantly associated with higher DASS21 scores (a higher DASS21 score means worse mental health status). Marital status and field were significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Age groups < 20 years (β* = 6.36, 95% CI: 0.78, 11.95) and income group < 5 million (β* = 6.58, 95% CI: 1.47, 11.70) increased mental health scores. Being single (β* = -34.72, 95% CI: -47.06, -38.78), being married (β* = -38.78, 95% CI: -51.23, -26.33) and in medical sciences fields (β* = -8.17, 95% CI: -12.09, -4.24) decreased DASS21 scores. (Table 3 )

The main purpose of this study was to determine the relationship between social media use and mental health among students during the COVID-19 pandemic.

University students are more reliant on social media because of the limitations of in-person interactions [ 45 ]. Since social media has become more and more vital to the social lives of university students during the pandemic, students may be at increased risk of social media addiction, which may be harmful to their mental health [ 14 ].

During non-adulthood, peer relations and approval are critical and social media seems to meet these needs. For example, connection and communication with friends make them feel better and happier, especially during the COVID-19 pandemic and national lockdowns where face-to-face communication was restricted [ 46 ]. Kele’s study showed that the COVID-19 pandemic has increased the time spent on social media, and the frequency of online activities [ 47 ].

Because of the COVID-19 pandemic, e-learning became the only sustainable option for students [ 13 ]. This abrupt transition can lead to depression, stress, or anxiety for some students due to insufficient time to adjust to the new learning environment. The role of social media is also important to some university students [ 48 ].

Staying at home, having nothing else to do, and being unable to go out and meet with friends due to the lockdown measures increased the time spent on social media and the frequency of online activities, which influenced their mental health negatively [ 49 ]. These reasons may explain the findings of previous studies that found an increase in depression and anxiety among adolescents who were healthy before the COVID-19 pandemic [ 50 ].

According to the results, there was a statistically significant relationship between social media use and mental health in students, in such a way that one Unit increase in the score of social media use enhanced the score of mental health. These two variables were directly correlated. Consistent with the current study, many studies have shown a significant relationship between higher use of social media and lower mental health in students [ 45 , 51 , 52 , 53 , 54 ].

Inconsistent with the findings of the present study, some previous studies reported the positive effect of social media use on mental health [ 55 , 56 , 57 ]. The differences in findings could be attributed to the time and location of the studies. Anderson’s study in France in 2018 found no significant relationship between social media use and mental health. This may be because of the differences between the tools for measuring the ability to detect fake news and health literacy and the scales of the research [ 4 ].

The present study showed that the impact of using social media on the mental health of students was higher than Lebni’s study, which was conducted in 2020 [ 25 ]. Also, in Dost Mohammad’s study in 2018, the effect of using social media on the mental health of students was reported to be lower than in the present study [ 58 ]. Entezari’s study in 2021, was also lower than the present study [ 59 ]. It seems that the excessive use of social media during the COVID-19 pandemic was the reason for the greater effects of social media on students’ mental health.

The use of social media has positive and negative characteristics. Social media is most useful for rapidly disseminating timely information via widely accessible platforms [ 4 ]. Among the types of studies, at least one shows an inverse relationship between the use of social media and mental health [ 53 ]. While social media can serve as a tool for fostering connection during periods of physical isolation, the mental health implications of social media being used as a news source are tenuous [ 45 ].

The results of the GLM analysis indicated that there was a statistically significant relationship between the problematic use of social media and mental health in students in such a way that one-unit increase in the score of problematic use of social media enhanced the mental health score, and it was found that the two variables had a direct relationship. Consistent with our study, Boer’s study showed that problematic use of social media may highlight the potential risk to adolescent mental health [ 60 ]. Malaeb also reported that the problematic use of social media had a positive relationship with mental health [ 61 ], but that study was conducted on adults and had a smaller sample size before the COVID-19 pandemic.

Saputri’s study found that excessive social media use likely harms the mental health of university students since students with higher social media addiction scores had a greater risk of experiencing mild depression [ 62 ]. A systematic literature review before the COVID-19 pandemic (2019) found that the time spent by adolescents on social media was associated with depression, anxiety, and psychological distress [ 63 ]. Marino’s study (2018) reported a significant correlation between the problematic use of social media by students and psychological distress [ 64 ].

Social media has become more vital for students’ social lives owing to online education during the COVID-19 pandemic. Therefore, this group is more at risk of addiction to social media and may experience more mental health problems than other groups. Lebni also indicated that students’ higher use of the Internet led to anxiety, depression, and adverse mental health, but the main purpose of the study was to investigate the effects of such factors on student’s academic performance [ 25 ]. Previous studies indicated that individuals who spent more time on social media had lower self-esteem and higher levels of anxiety and depression [ 65 , 66 ]. In the present study, students with higher social media addiction scores were at higher mental health risk. Such a finding was consistent with research by Gao et al., who found that the excessive use of social media during the pandemic had adverse effects on social health [ 14 ]. Cheng et al. indicated that using the Internet, especially for communication with people, can harm mental health by changing the quality of social relationships, face-to-face communication, and changes in social support [ 24 ].

A reason for the significant relationship between social media use and mental health in students during the COVID-19 pandemic in the present study was probably the students’ intentional or unintentional use of online communication. Unfortunately, social media published information, which might be incorrect, in this pandemic that caused public fear and threatened mental health.

During the pandemic, social media played essential roles in learning and leisure activities. Due to electronic education, staying at home, and long leisure time, students had more time, frequency, and opportunities to use social media in this pandemic. Such a high reliance on social media may threaten student’s mental health. Lee et al. conducted a study during the COVID-19 pandemic and confirmed that young people who used social media had higher symptoms of depression and loneliness than before the COVID-19 pandemic [ 67 ].

The present study showed that there was a significant positive relationship between problematic use of social media and gender, so that women were more willing to use social media, probably because they had more opportunities to use social media as they stayed at home more than men; hence, they were more exposed to problematic use of social media. Consistent with our study, Andreassen reported that being a woman was an important factor in social media addiction [ 68 ]. In contrast to our study, Azizi’s study in Iran showed that male students use social media significantly more than female students, possibly due to differences in demographic variables in each population [ 69 ].

Moreover, there was a significant relationship between age and problematic use of social media in that people younger than 20 were more willing to use social media in a problematic way. Consistent with the present study, Perrin also indicated that younger people further used social media [ 70 ].

According to the findings, unemployed students used social media more than employed ones, probably because they had more time to spend in virtual space, leading to higher use and the possibility of problematic use of social media [ 71 ].

Moreover, non-native students were more willing to use the social media probably because students who lived far away from their families used social media problematically due to the lack of family control over hours of use and higher opportunities [ 72 ] .

The results showed that rural students have a greater tendency to use social Medias than urban students. Inconsistent with this finding, Perrin reported that urban people were more willing to use the social media. The difference was probably due to different research times and places or different target groups [ 70 ].

According to the current study, people with low household income were more likely to use social media, most likely because low-income people seek free information and services due to a lack of access to facilities and equipment in the real world or because they seek assimilation with people around them. Inconsistent with our findings, Hruska et al. reported that people with high household income levels made much use of social media [ 73 ], probably because of cultural, economic, and social differences or different information measurement tools.

Furthermore, single, divorced, and widowed students used social media more than married students. This is because they spend more time on social media due to the need for more emotional attention, the search for a life partner, or a feeling of loneliness. This also led to the problematic use of social media [ 74 ].

According to the results, Fars people used social media more than other ethnic groups, but this difference was insignificant. This finding was consistent with Perrin’s study, but the population consisted of people aged 18 to 65 [ 70 ].

In the current study, there was a significant relationship between gender and mental health, so that women had lower mental health than men. The difference was in health sociology. Consistent with the present study, Ghasemi et al. indicated that it appeared necessary to pay more attention to women’s health and create an opportunity for them to use health services [ 75 ].

The findings revealed that unemployed students had lower mental health than employed students, most likely because unemployed individuals have lower mental health due to not having a job and being economically dependent on others, as well as feeling incompetent at times. Consistent with the present study, Bialowolski reported that unemployment and low income caused mental disorders and threatened mental health [ 76 ].

According to this study, non-native students have lower mental health than native students because they live far from their families. The family plays an imperative role in improving the mental health of their children, and mental health requires their support. Also, the economic, social, and support problems caused by being away from the family have endangered their mental health [ 77 ].

Another important factor of the current study was that married people had higher mental health than single people. In addition, divorced and widowed students had lower mental health [ 78 ]. Possibly due to the social pressure they suffer in Iranian society. Furthermore, they received lower emotional support than married people. Therefore, their lower mental health seemed logical [ 79 , 80 , 81 ]. A large study in a European population also reported differences in the likelihood of mood, anxiety, and personality disorders between separated/divorced and married mothers [ 82 ].

A key point confirmed in other studies is the relationship between low incomes with mental health. A meta-analysis by Lorant indicated that economic and social inequalities caused mental disorders [ 83 ]. Safran also reported that the probability of developing mental disorders in people with low socioeconomic status is up to three times higher than that of people with the highest socioeconomic status [ 84 ]. Bialowolski’s study was consistent with the current study but Bialowolski’s study examined employees [ 76 ].

The present study was conducted during the COVID-19 pandemic and therefore had limitations in accessing students. Another limitation was the use of self-reporting tools. Participants may show positive self-presentation by over- or under-reporting their social media-related behaviors and some mental health-related items, which may directly or indirectly lead to social desirability bias, information bias, and reporting bias. Small sample sizes and convenience sampling limit student population representativeness and generalizability. This study was based on cross-sectional data. Therefore, the estimation results should be seen as associative rather than causative. Future studies would need to investigate causal effects using a longitudinal or cohort design, or another causal effect research design.

The findings of this study indicated that the high use of social media affected students’ mental health. Furthermore, the problematic use of the social media had a direct relationship with mental health. Variables such as age, gender, income level, marital status, and unemployment of non-native students had significant relationships with social media use and mental health. Despite the large amount of evidence suggesting that social media harms mental health, more research is still necessary to determine the cause and how social media can be used without harmful effects. It is imperative to better understand the relationship between social media use and mental health symptoms among young people to prevent such a negative outcome.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to express their gratitude to all academic officials of Lorestan universities and Mr. Mohsen Amani for their cooperation in data collection.

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Abouzar Nazari

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Maede Hosseinnia

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Samaneh Torkian

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Abouzar Nazari and Maedeh Hossennia designed the study, collected the data and drafted the manuscript. Samaneh Torkian performed the statistical analysis and prepared the tables. Gholamreza Garmaroudi, as the responsible author, supervised the entire study. All authors reviewed and edited the draft manuscript and approved the final version.

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Nazari, A., Hosseinnia, M., Torkian, S. et al. Social media and mental health in students: a cross-sectional study during the Covid-19 pandemic. BMC Psychiatry 23 , 458 (2023). https://doi.org/10.1186/s12888-023-04859-w

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An Exploratory Study of Students with Depression in Undergraduate Research Experiences

Katelyn M. Cooper
Logan E. Gin
M. Elizabeth Barnes
Sara E. Brownell

*Address correspondence to: Katelyn M. Cooper ( E-mail Address: [email protected] ).

Department of Biology, University of Central Florida, Orlando, FL, 32816

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Biology Education Research Lab, Research for Inclusive STEM Education Center, School of Life Sciences, Arizona State University, Tempe, AZ 85281

Depression is a top mental health concern among undergraduates and has been shown to disproportionately affect individuals who are underserved and underrepresented in science. As we aim to create a more inclusive scientific community, we argue that we need to examine the relationship between depression and scientific research. While studies have identified aspects of research that affect graduate student depression, we know of no studies that have explored the relationship between depression and undergraduate research. In this study, we sought to understand how undergraduates’ symptoms of depression affect their research experiences and how research affects undergraduates’ feelings of depression. We interviewed 35 undergraduate researchers majoring in the life sciences from 12 research-intensive public universities across the United States who identify with having depression. Using inductive and deductive coding, we identified that students’ depression affected their motivation and productivity, creativity and risk-taking, engagement and concentration, and self-perception and socializing in undergraduate research experiences. We found that students’ social connections, experiencing failure in research, getting help, receiving feedback, and the demands of research affected students’ depression. Based on this work, we articulate an initial set of evidence-based recommendations for research mentors to consider in promoting an inclusive research experience for students with depression.

INTRODUCTION

Depression is described as a common and serious mood disorder that results in persistent feelings of sadness and hopelessness, as well as a loss of interest in activities that one once enjoyed ( American Psychiatric Association [APA], 2013 ). Additional symptoms of depression include weight changes, difficulty sleeping, loss of energy, difficulty thinking or concentrating, feelings of worthlessness or excessive guilt, and suicidality ( APA, 2013 ). While depression results from a complex interaction of psychological, social, and biological factors ( World Health Organization, 2018 ), studies have shown that increased stress caused by college can be a significant contributor to student depression ( Dyson and Renk, 2006 ).

Depression is one of the top undergraduate mental health concerns, and the rate of depression among undergraduates continues to rise ( Center for Collegiate Mental Health, 2017 ). While we cannot discern whether these increasing rates of depression are due to increased awareness or increased incidence, it is clear that is a serious problem on college campuses. The percent of U.S. college students who self-reported a diagnosis with depression was recently estimated to be about 25% ( American College Health Association, 2019 ). However, higher rates have been reported, with one study estimating that up to 84% of undergraduates experience some level of depression ( Garlow et al. , 2008 ). Depression rates are typically higher among university students compared with the general population, despite being a more socially privileged group ( Ibrahim et al. , 2013 ). Prior studies have found that depression is negatively correlated with overall undergraduate academic performance ( Hysenbegasi et al. , 2005 ; Deroma et al. , 2009 ; American College Health Association, 2019 ). Specifically, diagnosed depression is associated with half a letter grade decrease in students’ grade point average ( Hysenbegasi et al. , 2005 ), and 21.6% of undergraduates reported that depression negatively affected their academic performance within the last year ( American College Health Association, 2019 ). Provided with a list of academic factors that may be affected by depression, students reported that depression contributed to lower exam grades, lower course grades, and not completing or dropping a course.

Students in the natural sciences may be particularly at risk for depression, given that such majors are noted to be particularly stressful due to their competitive nature and course work that is often perceived to “weed students out”( Everson et al. , 1993 ; Strenta et al. , 1994 ; American College Health Association, 2019 ; Seymour and Hunter, 2019 ). Science course instruction has also been described to be boring, repetitive, difficult, and math-intensive; these factors can create an environment that can trigger depression ( Seymour and Hewitt, 1997 ; Osborne and Collins, 2001 ; Armbruster et al ., 2009 ; Ceci and Williams, 2010 ). What also distinguishes science degree programs from other degree programs is that, increasingly, undergraduate research experiences are being proposed as an essential element of a science degree ( American Association for the Advancement of Science, 2011 ; President’s Council of Advisors on Science and Technology, 2012 ; National Academies of Sciences, Engineering, and Medicine [NASEM], 2017 ). However, there is some evidence that undergraduate research experiences can add to the stress of college for some students ( Cooper et al. , 2019c ). Students can garner multiple benefits from undergraduate research, including enhanced abilities to think critically ( Ishiyama, 2002 ; Bauer and Bennett, 2003 ; Brownell et al. , 2015 ), improved student learning ( Rauckhorst et al. , 2001 ; Brownell et al. , 2015 ), and increased student persistence in undergraduate science degree programs ( Jones et al. , 2010 ; Hernandez et al. , 2018 ). Notably, undergraduate research experiences are increasingly becoming a prerequisite for entry into medical and graduate programs in science, particularly elite programs ( Cooper et al. , 2019d ). Although some research experiences are embedded into formal lab courses as course-based undergraduate research experiences (CUREs; Auchincloss et al. , 2014 ; Brownell and Kloser, 2015 ), the majority likely entail working with faculty in their research labs. These undergraduate research experiences in faculty labs are often added on top of a student’s normal course work, so they essentially become an extracurricular activity that they have to juggle with course work, working, and/or personal obligations ( Cooper et al. , 2019c ). While the majority of the literature surrounding undergraduate research highlights undergraduate research as a positive experience ( NASEM, 2017 ), studies have demonstrated that undergraduate research experiences can be academically and emotionally challenging for students ( Mabrouk and Peters, 2000 ; Seymour et al. , 2004 ; Cooper et al. , 2019c ; Limeri et al. , 2019 ). In fact, 50% of students sampled nationally from public R1 institutions consider leaving their undergraduate research experience prematurely, and about half of those students, or 25% of all students, ultimately leave their undergraduate research experience ( Cooper et al. , 2019c ). Notably, 33.8% of these individuals cited a negative lab environment and 33.3% cited negative relationships with their mentors as factors that influenced their decision about whether to leave ( Cooper et al. , 2019c ). Therefore, students’ depression may be exacerbated in challenging undergraduate research experiences, because studies have shown that depression is positively correlated with student stress ( Hish et al. , 2019 ).

While depression has not been explored in the context of undergraduate research experiences, depression has become a prominent concern surrounding graduate students conducting scientific research. A recent study that examined the “graduate student mental health crisis” ( Flaherty, 2018 ) found that work–life balance and graduate students’ relationships with their research advisors may be contributing to their depression ( Evans et al. , 2018 ). Specifically, this survey of 2279 PhD and master’s students from diverse fields of study, including the biological/physical sciences, showed that 39% of graduate students have experienced moderate to severe depression. Fifty-five percent of the graduate students with depression who were surveyed disagreed with the statement “I have good work life balance,” compared to only 21% of students with depression who agreed. Additionally, the study highlighted that more students with depression disagreed than agreed with the following statements: their advisors provided “real” mentorship, their advisors provided ample support, their advisors positively impacted their emotional or mental well-being, their advisors were assets to their careers, and they felt valued by their mentors. Another recent study identified that depression severity in biomedical doctoral students was significantly associated with graduate program climate, a perceived lack of employment opportunities, and the quality of students’ research training environment ( Nagy et al. , 2019 ). Environmental stress, academic stress, and family and monetary stress have also been shown to be predictive of depression severity in biomedical doctoral students ( Hish et al. , 2019 ). Further, one study found that self-esteem is negatively correlated and stress is positively correlated with graduate student depression; presumably research environments that challenge students’ self-esteem and induce stress are likely contributing to depressive symptoms among graduate students ( Kreger, 1995 ). While these studies have focused on graduate students, and there are certainly notable distinctions between graduate and undergraduate research, the research-related factors that affect graduate student depression, including work–life balance, relationships with mentors, research environment, stress, and self-esteem, may also be relevant to depression among undergraduates conducting research. Importantly, undergraduates in the United States have reported identical levels of depression as graduate students but are often less likely to seek mental health care services ( Wyatt and Oswalt, 2013 ), which is concerning if undergraduate research experiences exacerbate depression.

Based on the literature on the stressors of undergraduate research experiences and the literature identifying some potential causes of graduate student depression, we identified three aspects of undergraduate research that may exacerbate undergraduates’ depression. Mentoring: Mentors can be an integral part of a students’ research experience, bolstering their connections with others in the science community, scholarly productivity, and science identity, as well as providing many other benefits ( Thiry and Laursen, 2011 ; Prunuske et al. , 2013 ; Byars-Winston et al. , 2015 ; Aikens et al. , 2016 , 2017 ; Thompson et al. , 2016 ; Estrada et al. , 2018 ). However, recent literature has highlighted that poor mentoring can negatively affect undergraduate researchers ( Cooper et al. , 2019c ; Limeri et al. , 2019 ). Specifically, one study of 33 undergraduate researchers who had conducted research at 10 institutions identified seven major ways that they experienced negative mentoring, which included absenteeism, abuse of power, interpersonal mismatch, lack of career support, lack of psychosocial support, misaligned expectations, and unequal treatment ( Limeri et al. , 2019 ). We hypothesize negative mentoring experiences may be particularly harmful for students with depression, because support, particularly social support, has been shown to be important for helping individuals with depression cope with difficult circumstances ( Aneshensel and Stone, 1982 ; Grav et al. , 2012 ). Failure: Experiencing failure has been hypothesized to be an important aspect of undergraduate research experiences that may help students develop some the most distinguishing abilities of outstanding scientists, such as coping with failure, navigating challenges, and persevering ( Laursen et al. , 2010 ; Gin et al. , 2018 ; Henry et al. , 2019 ). However, experiencing failure and the stress and fatigue that often accompany it may be particularly tough for students with depression ( Aldwin and Greenberger, 1987 ; Mongrain and Blackburn, 2005 ). Lab environment: Fairness, inclusion/exclusion, and social support within one’s organizational environment have been shown to be key factors that cause people to either want to remain in the work place and be productive or to want to leave ( Barak et al. , 2006 ; Cooper et al. , 2019c ). We hypothesize that dealing with exclusion or a lack of social support may exacerbate depression for some students; patients with clinical depression react to social exclusion with more pronounced negative emotions than do individuals without clinical depression ( Jobst et al. , 2015 ). While there are likely other aspects of undergraduate research that affect student depression, we hypothesize that these factors have the potential to exacerbate negative research experiences for students with depression.

Depression has been shown to disproportionately affect many populations that are underrepresented or underserved within the scientific community, including females ( American College Health Association, 2018 ; Evans et al. , 2018 ), first-generation college students ( Jenkins et al. , 2013 ), individuals from low socioeconomic backgrounds ( Eisenberg et al. , 2007 ), members of the LGBTQ+ community ( Eisenberg et al. , 2007 ; Evans et al. , 2018 ), and people with disabilities ( Turner and Noh, 1988 ). Therefore, as the science community strives to be more diverse and inclusive ( Intemann, 2009 ), it is important that we understand more about the relationship between depression and scientific research, because negative experiences with depression in scientific research may be contributing to the underrepresentation of these groups. Specifically, more information is needed about how the research process and environment of research experiences may affect depression.

Given the high rate of depression among undergraduates, the links between depression and graduate research, the potentially challenging environment of undergraduate research, and how depression could disproportionately impact students from underserved communities, it is imperative to begin to explore the relationship between scientific research and depression among undergraduates to create research experiences that could maximize student success. In this exploratory interview study, we aimed to 1) describe how undergraduates’ symptoms of depression affect their research experiences, 2) understand how undergraduate research affects students’ feelings of depression, and 3) identify recommendations based on the literature and undergraduates’ reported experiences to promote a positive research experience for students with depression.

This study was done with an approved Arizona State University Institutional Review Board protocol #7247.

In Fall 2018, we surveyed undergraduate researchers majoring in the life sciences across 25 research-intensive (R1) public institutions across the United States (specific details about the recruitment of the students who completed the survey can be found in Cooper et al. (2019c) ). The survey asked students for their opinions about their undergraduate research experiences and their demographic information and whether they would be interested in participating in a follow-up interview related to their research experiences. For the purpose of this study, we exclusively interviewed students about their undergraduate research experiences in faculty member labs; we did not consider students’ experiences in CUREs. Of the 768 undergraduate researchers who completed the survey, 65% ( n = 496) indicated that they would be interested in participating in a follow-up interview. In Spring 2019, we emailed the 496 students, explaining that we were interested in interviewing students with depression about their experiences in undergraduate research. Our specific prompt was: “If you identify as having depression, we would be interested in hearing about your experience in undergraduate research in a 30–60 minute online interview.” We did not define depression in our email recruitment because we conducted think-aloud interviews with four undergraduates who all correctly interpreted what we meant by depression ( APA, 2013 ). We had 35 students agree to participate in the interview study. The interview participants represented 12 of the 25 R1 public institutions that were represented in the initial survey.

Student Interviews

We developed an interview script to explore our research questions. Specifically, we were interested in how students’ symptoms of depression affect their research experiences, how undergraduate research negatively affects student depression, and how undergraduate research positively affects student depression.

We recognized that mental health, and specifically depression, can be a sensitive topic to discuss with undergraduates, and therefore we tried to minimize any discomfort that the interviewees might experience during the interview. Specifically, we conducted think-aloud interviews with three graduate students who self-identified with having depression at the time of the interview. We asked them to note whether any interview questions made them uncomfortable. We also sought their feedback on questions given their experiences as persons with depression who had once engaged in undergraduate research. We revised the interview protocol after each think-aloud interview. Next, we conducted four additional think-aloud interviews with undergraduates conducting basic science or biology education research who identified with having depression to establish cognitive validity of the questions and to elicit additional feedback about any questions that might make someone uncomfortable. The questions were revised after each think-aloud interview until no question was unclear or misinterpreted by the students and we were confident that the questions minimized students’ potential discomfort ( Trenor et al. , 2011 ). A copy of the final interview script can be found in the Supplemental Material.

All interviews were individually conducted by one of two researchers (K.M.C. and L.E.G.) who conducted the think-aloud interviews together to ensure that their interviewing practices were as similar as possible. The interviews were approximately an hour long, and students received a $15 gift card for their participation.

Personal, Research, and Depression Demographics

All student demographics and information about students’ research experiences were collected using the survey distributed to students in Fall 2018. We collected personal demographics, including the participants’ gender, race/ethnicity, college generation status, transfer status, financial stability, year in college, major, and age. We also collected information about the students’ research experiences, including the length of their first research experiences, the average number of hours they spend in research per week, how they were compensated for research, who their primary mentors were, and the focus areas of their research.

In the United States, mental healthcare is disproportionately unavailable to Black and Latinx individuals, as well as those who come from low socioeconomic backgrounds ( Kataoka et al. , 2002 ; Howell and McFeeters, 2008 ; Santiago et al. , 2013 ). Therefore, to minimize a biased sample, we invited anyone who identified with having depression to participate in our study; we did not require students to be diagnosed with depression or to be treated for depression in order to participate. However, we did collect information about whether students had been formally diagnosed with depression and whether they had been treated for depression. After the interview, all participants were sent a link to a short survey that asked them if they had ever been diagnosed with depression and how, if at all, they had ever been treated for depression. A copy of these survey questions can be found in the Supplemental Material. The combined demographic information of the participants is in Table 1 . The demographics for each individual student can be found in the Supplemental Material.

Student-level demographics, research demographics, and depression demographics of the 35 interview participants
Student-level demographics	Interview participants = 35 (%)	Research demographics	Interview participants = 35 (%)	Depression demographics	Interview participants = 35 (%)

Female	27 (77%)	Less than 6 months	7 (20%)	Yes	21 (60%)
Male	7 (23%)	6 months	6 (17%)	No	10 (29%)
Declined to state	1 (3%)	1 year	11 (31%)	Declined to state	4 (11%)
		1.5 years	4 (11%)
Asian	9 (26%)	2 years	2 (6%)	Medication	15 (43%)
Black	1 (3%)	3 years	3 (9%)	Counseling	17 (49%)
Latinx	5 (14%)	3.5 years	1 (3%)	Other	2 (6%)
Middle Eastern	1 (3%)	Declined to state	1 (3%)	No treatment	15 (43%)
Mixed race	1 (3%)			Declined to state	2 (6%)
White	17 (49%)	1–5 hours	6 (17%)
Declined to state	1 (3%)	6–10 hours	16 (46%)
		11–15 hours	7 (20%)
First generation	10 (29%)	16 + hours	5 (14%)
Continuing generation	24 (69%)	Declined to state	1 (3%)
Declined to state	1 (3%)
		Money	13 (37%)
Transfer	5 (14%)	Course credit	24 (69%)
Nontransfer	29 (83%)	Volunteer	7 (20%)
Declined to state	1 (3%)	Declined to state	2 (6%)

No	6 (17%)	PI	9 (26%)
Yes, but only sometimes	12 (34%)	Postdoc	3 (9%)
Yes	16 (46%)	Graduate student	14 (40%)
Declined to state	1 (3%)	Staff member	7 (20%)
		Undergraduate student	1 (3%)
First year	1 (3%)	Declined to state	1 (3%)
Second year	5 (14%)
Third year	6 (17%)	Cell/molecular biology	4 (11%)
Fourth year or greater	22 (63%)	Ecology/evolution	9 (26%)
Declined to state	1 (3%)	Genetics	5 (14%)
		Immunology	4 (11%)
Biology	32 (91%)	Neuroscience	3 (9%)
Biochemistry	2 (6%)	Physiology/health	3 (9%)
Declined to state	1 (3%)	Other	6 (17%)
		Declined to state	1 (3%)
18–19	5 (14%)
20–21	17 (49%)
22–23	11 (31%)
24 or older	1 (3%)
Declined to state	1 (3%)

a Students reported the time they had spent in research 6 months before being interviewed and only reported on the length of time of their first research experiences.

b Students were invited to report multiple ways in which they were treated for their depression; other treatments included lifestyle changes and meditation.

c Students were invited to report multiple means of compensation for their research if they had been compensated for their time in different ways.

d Students were asked whether they felt financially stable, particularly during the undergraduate research experience.

e Students reported who they work/worked with most closely during their research experiences.

f Staff members included lab coordinators or lab managers.

g Other focus areas of research included sociology, linguistics, psychology, and public health.

Interview Analysis

The initial interview analysis aimed to explore each idea that a participant expressed ( Charmaz, 2006 ) and to identify reoccurring ideas throughout the interviews. First, three authors (K.M.C., L.E.G., and S.E.B.) individually reviewed a different set of 10 interviews and took detailed analytic notes ( Birks and Mills, 2015 ). Afterward, the authors compared their notes and identified reoccurring themes throughout the interviews using open coding methods ( Saldaña, 2015 ).

Once an initial set of themes was established, two researchers (K.M.C. and L.E.G.) individually reviewed the same set of 15 randomly selected interviews to validate the themes identified in the initial analysis and to screen for any additional themes that the initial analysis may have missed. Each researcher took detailed analytic notes throughout the review of an interview, which they discussed after reviewing each interview. The researchers compared what quotes from each interview they categorized into each theme. Using constant comparison methods, they assigned quotes to each theme and constantly compared the quotes to ensure that each quote fit within the description of the theme ( Glesne and Peshkin, 1992 ). In cases in which quotes were too different from other quotes, a new theme was created. This approach allowed for multiple revisions of the themes and allowed the authors to define a final set of codes; the researchers created a final codebook with refined definitions of emergent themes (the final coding rubric can be found in the Supplemental Material). Once the final codebook was established, the researchers (K.M.C. and L.E.G.) individually coded seven additional interviews (20% of all interviews) using the coding rubric. The researchers compared their codes, and their Cohen’s κ interrater score for these seven interviews was at an acceptable level (κ = 0.88; Landis and Koch, 1977 ). One researcher (L.E.G.) coded the remaining 28 out of 35 interviews. The researchers determined that data saturation had been reached with the current sample and no further recruitment was needed ( Guest et al. , 2006 ). We report on themes that were mentioned by at least 20% of students in the interview study. In the Supplemental Material, we provide the final coding rubric with the number of participants whose interview reflected each theme ( Hannah and Lautsch, 2011 ). Reporting the number of individuals who reported themes within qualitative data can lead to inaccurate conclusions about the generalizability of the results to a broader population. These qualitative data are meant to characterize a landscape of experiences that students with depression have in undergraduate research rather than to make claims about the prevalence of these experiences ( Glesne and Peshkin, 1992 ). Because inferences about the importance of these themes cannot be drawn from these counts, they are not included in the results of the paper ( Maxwell, 2010 ). Further, the limited number of interviewees made it not possible to examine whether there were trends based on students’ demographics or characteristics of their research experiences (e.g., their specific area of study). Quotes were lightly edited for clarity by inserting clarification brackets and using ellipses to indicate excluded text. Pseudonyms were given to all students to protect their privacy.

The Effect of Depressive Symptoms on Undergraduate Research

We asked students to describe the symptoms associated with their depression. Students described experiencing anxiety that is associated with their depression; this could be anxiety that precedes their depression or anxiety that results from a depressive episode or a period of time when an individual has depression symptoms. Further, students described difficulty getting out of bed or leaving the house, feeling tired, a lack of motivation, being overly self-critical, feeling apathetic, and having difficulty concentrating. We were particularly interested in how students’ symptoms of depression affected their experiences in undergraduate research. During the think-aloud interviews that were conducted before the interview study, graduate and undergraduate students consistently described that their depression affected their motivation in research, their creativity in research, and their productivity in research. Therefore, we explicitly asked undergraduate researchers how, if at all, their depression affected these three factors. We also asked students to describe any additional ways in which their depression affected their research experiences. Undergraduate researchers commonly described five additional ways in which their depression affected their research; for a detailed description of each way students’ research was affected and for example quotes, see Table 2 . Students described that their depression negatively affected their productivity in the lab. Commonly, students described that their productivity was directly affected by a lack of motivation or because they felt less creative, which hindered the research process. Additionally, students highlighted that they were sometimes less productive because their depression sometimes caused them to struggle to engage intellectually with their research or caused them to have difficulty remembering or concentrating; students described that they could do mundane or routine tasks when they felt depressed, but that they had difficulty with more complex and intellectually demanding tasks. However, students sometimes described that even mundane tasks could be difficult when they were required to remember specific steps; for example, some students struggled recalling a protocol from memory when their depression was particularly severe. Additionally, students noted that their depression made them more self-conscious, which sometimes held them back from sharing research ideas with their mentors or from taking risks such as applying to competitive programs. In addition to being self-conscious, students highlighted that their depression caused them to be overly self-critical, and some described experiencing imposter phenomenon ( Clance and Imes, 1978 ) or feeling like they were not talented enough to be in research and were accepted into a lab by a fluke or through luck. Finally, students described that depression often made them feel less social, and they struggled to socially engage with other members of the lab when they were feeling down.

Ways in which students report that depression affected their undergraduate research experience with example student quotes
	Description	Example quote 1	Example quote 2
Motivation and productivity
Lack of motivation in research	Students describe that their depression can cause them to feel unmotivated to do research.	Crystal: “[Depression] can make it hard to motivate myself to keep doing [research] because when I get into [depression] it doesn’t matter. [All my organisms] are going to die and everything’s going to go horribly sideways and why do I even bother? And then that can descend into a state of just sadness or apathy or a combination of the two.”	Naomi: “I don’t feel as motivated to do the research because I just don’t feel like doing anything. [Depression] definitely does not help with the motivation.”
Less productive	Students describe that depression can cause them to be less productive, less efficient, or to move slower than usual.	Marta: “I think at times when [my depression is] really, really bad, I’ll just find myself just sitting at my desk looking busy but not actually doing anything. (…) And I think that obviously affects productivity because I’m not really doing anything.”	Julie: “I think I literally moved and thought slower. (…) I think that if I could redo all of that time while not depressed, I would have gotten so much more done. I feel like so much of this stalling I had on various projects was because of [my depression].”
Creativity and risk-taking
Lack of creativity in research	Students describe that depression can cause them to be less creative in their research.	Michelle: “In that depressive episode, I probably won’t be even using my brain in that, sort of, [creative] sense. My mind will probably be just so limited and blank and I won’t even want to think creatively.”	Amy: “I think [depression] definitely has super negatively impacted my research creativity. I just feel like I’m not as creative with my problem solving skills when I am depressed as when I am not depressed.”
Held back from taking risks or contributing thoughts and ideas	Students describe that their depression can hold them back from sharing an idea with their lab mates or from taking risks like applying for competitive positions or trying something in research that might not work.	Marta: “[Depression affects my research] because I’m so scared to take a risk. That has really put a very short cap on what I’ve been able to do. And maybe I would’ve been able to get internships at institutions like my peers. But instead, because I was so limited by my depression, it kept me from doing that.”	Christian: “That’s where I think [depression] definitely negatively affects what I have accomplished just because I feel personally that I could have achieved more if I wasn’t held down, I guess, by depression. So, I feel like I would’ve been able to put myself out there more and take more risks, reaching out to others to take opportunities when I was in lab.”
Engagement and concentration
Struggle to intellectually engage	Students describe that they struggle to do research activities that require intellectual engagement when they are feeling depressed.	Freddy: “I find mechanical things like actually running an experiment in the lab, I can pretty much do regardless of how I’m feeling. But things that require a ton of mental energy, like analyzing data, doing statistics, or actually writing, was [ ] a lot more difficult if I was feeling depressed.”	Rose: “When you’re working on a research project you’re like ‘I wonder what this does? Or why is that the way it is?,’ and then you’ll read more articles and talk to a few people. And when I’m depressed, I don’t care. I’m like this is just another thing I have to do.”
Difficulty concentrating or remembering	Students describe that, because of their depression, they can have difficulty concentrating or remembering when they are conducting research.	Julie: “My memory absolutely goes to hell, especially my short-term memory. My attention span nosedives. Later, I will look back on work and have no idea how any of that made sense to me.”	Adrianna: “Yeah. [Sometimes when I’m depressed] it’s like, ‘Oh, I forgot a step,’ or ‘Oh, I mislabeled the tube.’ It’s like, okay, I got to slow down even more and pay more attention. But it’s really hard to get myself to focus.”
Self-perception and socializing
Overly self-critical	Students describe that depression causes them to have low self-esteem or to be overly self-critical.	Heather: “I guess [my depression can cause me to] beat myself up about different things. Especially when the experiment didn’t really work. I guess blaming myself to the point where it was unhealthy about different things. If I had an experiment and it didn’t work, even if I was working with someone else, then I’d put all the blame on myself. I guess [your depression] worsens it because you just feel worse about yourself mentally.”	Taylor: “I feel like I’m sort of not good enough, right? And I’ve sort of fooled [my research advisor] for letting me into their lab, and that I should just stop. I guess that’s really how [my depression] would relate directly to research.”
Less social	Students describe that their depression can cause them to not want to interact with others in the lab or to be less social in general.	Adrianna: “There are days I’m emotionally flat and obviously those I just don’t engage in conversation as much and [my lab mates] are probably like, ‘Oh, she’s just under the weather.’ I don’t know. It just affects my ability to want to sit down and talk to somebody.”	Michelle: “When I’m depressed I won’t talk as much, so [my lab mates and I] won’t have a conversation.”

The Effect of Undergraduate Research Experiences on Student Depression

We also wanted to explore how research impacted students’ feelings of depression. Undergraduates described how research both positively and negatively affected their depression. In the following sections, we present aspects of undergraduate research and examine how each positively and/or negatively affected students’ depression using embedded student quotes to highlight the relationships between related ideas.

Lab Environment: Relationships with Others in the Lab.

Some aspects of the lab environment, which we define as students’ physical, social, or psychological research space, could be particularly beneficial for students with depression.

Specifically, undergraduate researchers perceived that comfortable and positive social interactions with others in the lab helped their depression. Students acknowledged how beneficial their relationships with graduate students and postdocs could be.

Marta: “I think always checking in on undergrads is important. It’s really easy [for us] to go a whole day without talking to anybody in the lab. But our grad students are like ‘Hey, what’s up? How’s school? What’s going on?’ (…) What helps me the most is having that strong support system. Sometimes just talking makes you feel better, but also having people that believe in you can really help you get out of that negative spiral. I think that can really help with depression.”

Kelley: “I know that anytime I need to talk to [my postdoc mentors] about something they’re always there for me. Over time we’ve developed a relationship where I know that outside of work and outside of the lab if I did want to talk to them about something I could talk to them. Even just talking to someone about hobbies and having that relationship alone is really helpful [for depression].”

In addition to highlighting the importance of developing relationships with graduate students or postdocs in the lab, students described that forming relationships with other undergraduates in the lab also helped their depression. Particularly, students described that other undergraduate researchers often validated their feelings about research, which in turn helped them realize that what they are thinking or feeling is normal, which tended to alleviate their negative thoughts. Interestingly, other undergraduates experiencing the same issues could sometimes help buffer them from perceiving that a mentor did not like them or that they were uniquely bad at research. In this article, we use the term “mentor” to refer to anyone who students referred to in the interviews as being their mentors or managing their research experiences; this includes graduate students, postdoctoral scholars, lab managers, and primary investigators (PIs).

Abby: “One of my best friends is in the lab with me. A lot of that friendship just comes from complaining about our stress with the lab and our annoyance with people in the lab. Like when we both agree like, ‘Yeah, the grad students were really off today, it wasn’t us,’ that helps. ‘It wasn’t me, it wasn’t my fault that we were having a rough day in lab; it was the grad students.’ Just being able to realize, ‘Hey, this isn’t all caused by us,’ you know? (…) We understand the stresses in the lab. We understand the details of what each other are doing in the lab, so when something doesn’t work out, we understand that it took them like eight hours to do that and it didn’t work. We provide empathy on a different level.”

Meleana: “It’s great to have solidarity in being confused about something, and it’s just that is a form of validation for me too. When we leave a lab meeting and I look at [another undergrad] I’m like, ‘Did you understand anything that they were just saying?’ And they’re like, ‘Oh, no.’ (…) It’s just really validating to hear from the other undergrads that we all seem to be struggling with the same things.”

Developing positive relationships with faculty mentors or PIs also helped alleviate some students’ depressive feelings, particularly when PIs shared their own struggles with students. This also seemed to normalize students’ concerns about their own experiences.

Alexandra: “[Talking with my PI] is helpful because he would talk about his struggles, and what he faced. A lot of it was very similar to my struggles. For example, he would say, ‘Oh, yeah, I failed this exam that I studied so hard for. I failed the GRE and I paid so much money to prepare for it.’ It just makes [my depression] better, like okay, this is normal for students to go through this. It’s not an out of this world thing where if you fail, you’re a failure and you can’t move on from it.”

Students’ relationships with others in the lab did not always positively impact their depression. Students described instances when the negative moods of the graduate students and PIs would often set the tone of the lab, which in turn worsened the mood of the undergraduate researchers.

Abby: “Sometimes [the grad students] are not in a good mood. The entire vibe of the lab is just off, and if you make a joke and it hits somebody wrong, they get all mad. It really depends on the grad students and the leadership and the mood that they’re in.”

Interviewer: “How does it affect your depression when the grad students are in a bad mood?”

Abby: “It definitely makes me feel worse. It feels like, again, that I really shouldn’t go ask them for help because they’re just not in the mood to help out. It makes me have more pressure on myself, and I have deadlines I need to meet, but I have a question for them, but they’re in a bad mood so I can’t ask. That’s another day wasted for me and it just puts more stress, which just adds to the depression.”

Additionally, some students described even more concerning behavior from research mentors, which negatively affected their depression.

Julie: “I had a primary investigator who is notorious in the department for screaming at people, being emotionally abusive, unreasonable, et cetera. (…) [He was] kind of harassing people, demeaning them, lying to them, et cetera, et cetera. (…) Being yelled at and constantly demeaned and harassed at all hours of the day and night, that was probably pretty bad for me.”

While the relationships between undergraduates and graduate, postdoc, and faculty mentors seemed to either alleviate or worsen students’ depressive symptoms, depending on the quality of the relationship, students in this study exclusively described their relationships with other undergraduates as positive for their depression. However, students did note that undergraduate research puts some of the best and brightest undergraduates in the same environment, which can result in students comparing themselves with their peers. Students described that this comparison would often lead them to feel badly about themselves, even though they would describe their personal relationship with a person to be good.

Meleana: “In just the research field in general, just feeling like I don’t really measure up to the people around me [can affect my depression]. A lot of the times it’s the beginning of a little spiral, mental spiral. There are some past undergrads that are talked about as they’re on this pedestal of being the ideal undergrads and that they were just so smart and contributed so much to the lab. I can never stop myself from wondering like, ‘Oh, I wonder if I’m having a contribution to the lab that’s similar or if I’m just another one of the undergrads that does the bare minimum and passes through and is just there.’”

Natasha: “But, on the other hand, [having another undergrad in the lab] also reminded me constantly that some people are invested in this and meant to do this and it’s not me. And that some people know a lot more than I do and will go further in this than I will.”

While students primarily expressed that their relationships with others in the lab affected their depression, some students explained that they struggled most with depression when the lab was empty; they described that they did not like being alone in the lab, because a lack of stimulation allowed their minds to be filled with negative thoughts.

Mia: “Those late nights definitely didn’t help [my depression]. I am alone, in the entire building. I’m left alone to think about my thoughts more, so not distracted by talking to people or interacting with people. I think more about how I’m feeling and the lack of progress I’m making, and the hopelessness I’m feeling. That kind of dragged things on, and I guess deepened my depression.”

Freddy: “Often times when I go to my office in the evening, that is when I would [ sic ] be prone to be more depressed. It’s being alone. I think about myself or mistakes or trying to correct mistakes or whatever’s going on in my life at the time. I become very introspective. I think I’m way too self-evaluating, way too self-deprecating and it’s when I’m alone when those things are really, really triggered. When I’m talking with somebody else, I forget about those things.”

In sum, students with depression highlighted that a lab environment full of positive and encouraging individuals was helpful for their depression, whereas isolating or competitive environments and negative interactions with others often resulted in more depressive feelings.

Doing Science: Experiencing Failure in Research, Getting Help, Receiving Feedback, Time Demands, and Important Contributions.

In addition to the lab environment, students also described that the process of doing science could affect their depression. Specifically, students explained that a large contributor to their depression was experiencing failure in research.

Interviewer: “Considering your experience in undergraduate research, what tends to trigger your feelings of depression?”

Heather: “Probably just not getting things right. Having to do an experiment over and over again. You don’t get the results you want. (…) The work is pretty meticulous and it’s frustrating when I do all this work, I do a whole experiment, and then I don’t get any results that I can use. That can be really frustrating. It adds to the stress. (…) It’s hard because you did all this other stuff before so you can plan for the research, and then something happens and all the stuff you did was worthless basically.”

Julie: “I felt very negatively about myself [when a project failed] and pretty panicked whenever something didn’t work because I felt like it was a direct reflection on my effort and/or intelligence, and then it was a big glaring personal failure.”

Students explained that their depression related to failing in research was exacerbated if they felt as though they could not seek help from their research mentors. Perceived insufficient mentor guidance has been shown to be a factor influencing student intention to leave undergraduate research ( Cooper et al. , 2019c ). Sometimes students talked about their research mentors being unavailable or unapproachable.

Michelle: “It just feels like [the graduate students] are not approachable. I feel like I can’t approach them to ask for their understanding in a certain situation. It makes [my depression] worse because I feel like I’m stuck, and that I’m being limited, and like there’s nothing I can do. So then I kind of feel like it’s my fault that I can’t do anything.”

Other times, students described that they did not seek help in fear that they would be negatively evaluated in research, which is a fear of being judged by others ( Watson and Friend, 1969 ; Weeks et al. , 2005 ; Cooper et al. , 2018 ). That is, students fear that their mentor would think negatively about them or judge them if they were to ask questions that their mentor thought they should know the answer to.

Meleana: “I would say [my depression] tends to come out more in being more reserved in asking questions because I think that comes more like a fear-based thing where I’m like, ‘Oh, I don’t feel like I’m good enough and so I don’t want to ask these questions because then my mentors will, I don’t know, think that I’m dumb or something.’”

Conversely, students described that mentors who were willing to help them alleviated their depressive feelings.

Crystal: “Yeah [my grad student] is always like, ‘Hey, I can check in on things in the lab because you’re allowed to ask me for that, you’re not totally alone in this,’ because he knows that I tend to take on all this responsibility and I don’t always know how to ask for help. He’s like, ‘You know, this is my lab too and I am here to help you as well,’ and just reminds me that I’m not shouldering this burden by myself.”

Ashlyn: “The graduate student who I work with is very kind and has a lot of patience and he really understands a lot of things and provides simple explanations. He does remind me about things and he will keep on me about certain tasks that I need to do in an understanding way, and it’s just because he’s patient and he listens.”

In addition to experiencing failure in science, students described that making mistakes when doing science also negatively affected their depression.

Abby: “I guess not making mistakes on experiments [is important in avoiding my depression]. Not necessarily that your experiment didn’t turn out to produce the data that you wanted, but just adding the wrong enzyme or messing something up like that. It’s like, ‘Oh, man,’ you know? You can get really down on yourself about that because it can be embarrassing.”

Commonly, students described that the potential for making mistakes increased their stress and anxiety regarding research; however, they explained that how other people responded to a potential mistake was what ultimately affected their depression.

Briana: “Sometimes if I made a mistake in correctly identifying an eye color [of a fly], [my PI] would just ridicule me in front of the other students. He corrected me but his method of correcting was very discouraging because it was a ridicule. It made the others laugh and I didn’t like that.”

Julie: “[My PI] explicitly [asked] if I had the dedication for science. A lot of times he said I had terrible judgment. A lot of times he said I couldn’t be trusted. Once I went to a conference with him, and, unfortunately, in front of another professor, he called me a klutz several times and there was another comment about how I never learn from my mistakes.”

When students did do things correctly, they described how important it could be for them to receive praise from their mentors. They explained that hearing praise and validation can be particularly helpful for students with depression, because their thoughts are often very negative and/or because they have low self-esteem.

Crystal: “[Something that helps my depression is] I have text messages from [my graduate student mentor] thanking me [and another undergraduate researcher] for all of the work that we’ve put in, that he would not be able to be as on track to finish as he is if he didn’t have our help.”

Interviewer: “Why is hearing praise from your mentor helpful?”

Crystal: “Because a lot of my depression focuses on everybody secretly hates you, nobody likes you, you’re going to die alone. So having that validation [from my graduate mentor] is important, because it flies in the face of what my depression tells me.”

Brian: “It reminds you that you exist outside of this negative world that you’ve created for yourself, and people don’t see you how you see yourself sometimes.”

Students also highlighted how research could be overwhelming, which negatively affected their depression. Particularly, students described that research demanded a lot of their time and that their mentors did not always seem to be aware that they were juggling school and other commitments in addition to their research. This stress exacerbated their depression.

Rose: “I feel like sometimes [my grad mentors] are not very understanding because grad students don’t take as many classes as [undergrads] do. I think sometimes they don’t understand when I say I can’t come in at all this week because I have finals and they’re like, ‘Why though?’”

Abby: “I just think being more understanding of student life would be great. We have classes as well as the lab, and classes are the priority. They forget what it’s like to be a student. You feel like they don’t understand and they could never understand when you say like, ‘I have three exams this week,’ and they’re like, ‘I don’t care. You need to finish this.’”

Conversely, some students reported that their research labs were very understanding of students’ schedules. Interestingly, these students talked most about how helpful it was to be able to take a mental health day and not do research on days when they felt down or depressed.

Marta: “My lab tech is very open, so she’ll tell us, ‘I can’t come in today. I have to take a mental health day.’ So she’s a really big advocate for that. And I think I won’t personally tell her that I’m taking a mental health day, but I’ll say, ‘I can’t come in today, but I’ll come in Friday and do those extra hours.’ And she’s like, ‘OK great, I’ll see you then.’ And it makes me feel good, because it helps me take care of myself first and then I can take care of everything else I need to do, which is amazing.”

Meleana: “Knowing that [my mentors] would be flexible if I told them that I’m crazy busy and can’t come into work nearly as much this week [helps my depression]. There is flexibility in allowing me to then care for myself.”

Interviewer: “Why is the flexibility helpful given the depression?”

Meleana: “Because sometimes for me things just take a little bit longer when I’m feeling down. I’m just less efficient to be honest, and so it’s helpful if I feel like I can only go into work for 10 hours in a week. It declutters my brain a little bit to not have to worry about all the things I have to do in work in addition the things that I need to do for school or clubs, or family or whatever.”

Despite the demanding nature of research, a subset of students highlighted that their research and research lab provided a sense of stability or familiarity that distracted them from their depression.

Freddy: “I’ll [do research] to run away from those [depressive] feelings or whatever. (…) I find sadly, I hate to admit it, but I do kind of run to [my lab]. I throw myself into work to distract myself from the feelings of depression and sadness.”

Rose: “When you’re sad or when you’re stressed you want to go to things you’re familiar with. So because lab has always been in my life, it’s this thing where it’s going to be there for me I guess. It’s like a good book that you always go back to and it’s familiar and it makes you feel good. So that’s how lab is. It’s not like the greatest thing in the world but it’s something that I’m used to, which is what I feel like a lot of people need when they’re sad and life is not going well.”

Many students also explained that research positively affects their depression because they perceive their research contribution to be important.

Ashlyn: “I feel like I’m dedicating myself to something that’s worthy and something that I believe in. It’s really important because it contextualizes those times when I am feeling depressed. It’s like, no, I do have these better things that I’m working on. Even when I don’t like myself and I don’t like who I am, which is again, depression brain, I can at least say, ‘Well, I have all these other people relying on me in research and in this area and that’s super important.’”

Jessica: “I mean, it just felt like the work that I was doing had meaning and when I feel like what I’m doing is actually going to contribute to the world, that usually really helps with [depression] because it’s like not every day you can feel like you’re doing something impactful.”

In sum, students highlighted that experiencing failure in research and making mistakes negatively contributed to depression, especially when help was unavailable or research mentors had a negative reaction. Additionally, students acknowledged that the research could be time-consuming, but that research mentors who were flexible helped assuage depressive feelings that were associated with feeling overwhelmed. Finally, research helped some students’ depression, because it felt familiar, provided a distraction from depression, and reminded students that they were contributing to a greater cause.

We believe that creating more inclusive research environments for students with depression is an important step toward broadening participation in science, not only to ensure that we are not discouraging students with depression from persisting in science, but also because depression has been shown to disproportionately affect underserved and underrepresented groups in science ( Turner and Noh, 1988 ; Eisenberg et al. , 2007 ; Jenkins et al. , 2013 ; American College Health Association, 2018 ). We initially hypothesized that three features of undergraduate research—research mentors, the lab environment, and failure—may have the potential to exacerbate student depression. We found this to be true; students highlighted that their relationships with their mentors as well as the overall lab environment could negatively affect their depression, but could also positively affect their research experiences. Students also noted that they struggled with failure, which is likely true of most students, but is known to be particularly difficult for students with depression ( Elliott et al. , 1997 ). We expand upon our findings by integrating literature on depression with the information that students provided in the interviews about how research mentors can best support students. We provide a set of evidence-based recommendations focused on mentoring, the lab environment, and failure for research mentors wanting to create more inclusive research environments for students with depression. Notably, only the first recommendation is specific to students with depression; the others reflect recommendations that have previously been described as “best practices” for research mentors ( NASEM, 2017 , 2019 ; Sorkness et al. , 2017 ) and likely would benefit most students. However, we examine how these recommendations may be particularly important for students with depression. As we hypothesized, these recommendations directly address three aspects of research: mentors, lab environment, and failure. A caveat of these recommendations is that more research needs to be done to explore the experiences of students with depression and how these practices actually impact students with depression, but our national sample of undergraduate researchers with depression can provide an initial starting point for a discussion about how to improve research experiences for these students.

Recommendations to Make Undergraduate Research Experiences More Inclusive for Students with Depression

Recognize student depression as a valid illness..

Allow students with depression to take time off of research by simply saying that they are sick and provide appropriate time for students to recover from depressive episodes. Also, make an effort to destigmatize mental health issues.

Undergraduate researchers described both psychological and physical symptoms that manifested as a result of their depression and highlighted how such symptoms prevented them from performing to their full potential in undergraduate research. For example, students described how their depression would cause them to feel unmotivated, which would often negatively affect their research productivity. In cases in which students were motivated enough to come in and do their research, they described having difficulty concentrating or engaging in the work. Further, when doing research, students felt less creative and less willing to take risks, which may alter the quality of their work. Students also sometimes struggled to socialize in the lab. They described feeling less social and feeling overly self-critical. In sum, students described that, when they experienced a depressive episode, they were not able to perform to the best of their ability, and it sometimes took a toll on them to try to act like nothing was wrong, when they were internally struggling with depression. We recommend that research mentors treat depression like any other physical illness; allowing students the chance to recover when they are experiencing a depressive episode can be extremely important to students and can allow them to maximize their productivity upon returning to research ( Judd et al. , 2000 ). Students explained that if they are not able to take the time to focus on recovering during a depressive episode, then they typically continue to struggle with depression, which negatively affects their research. This sentiment is echoed by researchers in psychiatry who have found that patients who do not fully recover from a depressive episode are more likely to relapse and to experience chronic depression ( Judd et al. , 2000 ). Students described not doing tasks or not showing up to research because of their depression but struggling with how to share that information with their research mentors. Often, students would not say anything, which caused them anxiety because they were worried about what others in the lab would say to them when they returned. Admittedly, many students understood why this behavior would cause their research mentors to be angry or frustrated, but they weighed the consequences of their research mentors’ displeasure against the consequences of revealing their depression and decided it was not worth admitting to being depressed. This aligns with literature that suggests that when individuals have concealable stigmatized identities, or identities that can be hidden and that carry negative stereotypes, such as depression, they will often keep them concealed to avoid negative judgment or criticism ( Link and Phelan, 2001 ; Quinn and Earnshaw, 2011 ; Jones and King, 2014 ; Cooper and Brownell, 2016 ; Cooper et al. , 2019b ; Cooper et al ., unpublished data ). Therefore, it is important for research mentors to be explicit with students that 1) they recognize mental illness as a valid sickness and 2) that students with mental illness can simply explain that they are sick if they need to take time off. This may be useful to overtly state on a research website or in a research syllabus, contract, or agreement if mentors use such documents when mentoring undergraduates in their lab. Further, research mentors can purposefully work to destigmatize mental health issues by explicitly stating that struggling with mental health issues, such as depression and anxiety, is common. While we do not recommend that mentors ask students directly about depression, because this can force students to share when they are not comfortable sharing, we do recommend providing opportunities for students to reveal their depression ( Chaudoir and Fisher, 2010 ). Mentors can regularly check in with students about how they’re doing, and talk openly about the importance of mental health, which may increase the chance that students may feel comfortable revealing their depression ( Chaudoir and Quinn, 2010 ; Cooper et al ., unpublished data ).

Foster a Positive Lab Environment.

Encourage positivity in the research lab, promote working in shared spaces to enhance social support among lab members, and alleviate competition among undergraduates.

Students in this study highlighted that the “leadership” of the lab, meaning graduate students, postdocs, lab managers, and PIs, were often responsible for establishing the tone of the lab; that is, if they were in a bad mood it would trickle down and negatively affect the moods of the undergraduates. Explicitly reminding lab leadership that their moods can both positively and negatively affect undergraduates may be important in establishing a positive lab environment. Further, students highlighted how they were most likely to experience negative thoughts when they were alone in the lab. Therefore, it may be helpful to encourage all lab members to work in a shared space to enhance social interactions among students and to maximize the likelihood that undergraduates have access to help when needed. A review of 51 studies in psychiatry supported our undergraduate researchers’ perceptions that social relationships positively impacted their depression; the study found that perceived emotional support (e.g., someone available to listen or give advice), perceived instrumental support (e.g., someone available to help with tasks), and large diverse social networks (e.g., being socially connected to a large number of people) were significantly protective against depression ( Santini et al. , 2015 ). Additionally, despite forming positive relationships with other undergraduates in the lab, many undergraduate researchers admitted to constantly comparing themselves with other undergraduates, which led them to feel inferior, negatively affecting their depression. Some students talked about mentors favoring current undergraduates or talking positively about past undergraduates, which further exacerbated their feelings of inferiority. A recent study of students in undergraduate research experiences highlighted that inequitable distribution of praise to undergraduates can create negative perceptions of lab environments for students (Cooper et al. , 2019). Further, the psychology literature has demonstrated that when people feel insecure in their social environments, it can cause them to focus on a hierarchical view of themselves and others, which can foster feelings of inferiority and increase their vulnerability to depression ( Gilbert et al. , 2009 ). Thus, we recommend that mentors be conscious of their behaviors so that they do not unintentionally promote competition among undergraduates or express favoritism toward current or past undergraduates. Praise is likely best used without comparison with others and not done in a public way, although more research on the impact of praise on undergraduate researchers needs to be done. While significant research has been done on mentoring and mentoring relationships in the context of undergraduate research ( Byars-Winston et al. , 2015 ; Aikens et al. , 2017 ; Estrada et al. , 2018 ; Limeri et al. , 2019 ; NASEM, 2019 ), much less has been done on the influence of the lab environment broadly and how people in nonmentoring roles can influence one another. Yet, this study indicates the potential influence of many different members of the lab, not only their mentors, on students with depression.

Develop More Personal Relationships with Undergraduate Researchers and Provide Sufficient Guidance.

Make an effort to establish more personal relationships with undergraduates and ensure that they perceive that they have access to sufficient help and guidance with regard to their research.

When we asked students explicitly how research mentors could help create more inclusive environments for undergraduate researchers with depression, students overwhelmingly said that building mentor–student relationships would be extremely helpful. Students suggested that mentors could get to know students on a more personal level by asking about their career interests or interests outside of academia. Students also remarked that establishing a more personal relationship could help build the trust needed in order for undergraduates to confide in their research mentors about their depression, which they perceived would strengthen their relationships further because they could be honest about when they were not feeling well or their mentors might even “check in” with them in times where they were acting differently than normal. This aligns with studies showing that undergraduates are most likely to reveal a stigmatized identity, such as depression, when they form a close relationship with someone ( Chaudoir and Quinn, 2010 ). Many were intimidated to ask for research-related help from their mentors and expressed that they wished they had established a better relationship so that they would feel more comfortable. Therefore, we recommend that research mentors try to establish relationships with their undergraduates and explicitly invite them to ask questions or seek help when needed. These recommendations are supported by national recommendations for mentoring ( NASEM, 2019 ) and by literature that demonstrates that both social support (listening and talking with students) and instrumental support (providing students with help) have been shown to be protective against depression ( Santini et al. , 2015 ).

Treat Undergraduates with Respect and Remember to Praise Them.

Avoid providing harsh criticism and remember to praise undergraduates. Students with depression often have low self-esteem and are especially self-critical. Therefore, praise can help calibrate their overly negative self-perceptions.

Students in this study described that receiving criticism from others, especially harsh criticism, was particularly difficult for them given their depression. Multiple studies have demonstrated that people with depression can have an abnormal or maladaptive response to negative feedback; scientists hypothesize that perceived failure on a particular task can trigger failure-related thoughts that interfere with subsequent performance ( Eshel and Roiser, 2010 ). Thus, it is important for research mentors to remember to make sure to avoid unnecessarily harsh criticisms that make students feel like they have failed (more about failure is described in the next recommendation). Further, students with depression often have low self-esteem or low “personal judgment of the worthiness that is expressed in the attitudes the individual holds towards oneself” ( Heatherton et al. , 2003 , p. 220; Sowislo and Orth, 2013 ). Specifically, a meta-analysis of longitudinal studies found that low self-esteem is predictive of depression ( Sowislo and Orth, 2013 ), and depression has also been shown to be highly related to self-criticism ( Luyten et al. , 2007 ). Indeed, nearly all of the students in our study described thinking that they are “not good enough,” “worthless,” or “inadequate,” which is consistent with literature showing that people with depression are self-critical ( Blatt et al. , 1982 ; Gilbert et al. , 2006 ) and can be less optimistic of their performance on future tasks and rate their overall performance on tasks less favorably than their peers without depression ( Cane and Gotlib, 1985 ). When we asked students what aspects of undergraduate research helped their depression, students described that praise from their mentors was especially impactful, because they thought so poorly of themselves and they needed to hear something positive from someone else in order to believe it could be true. Praise has been highlighted as an important aspect of mentoring in research for many years ( Ashford, 1996 ; Gelso and Lent, 2000 ; Brown et al. , 2009 ) and may be particularly important for students with depression. In fact, praise has been shown to enhance individuals’ motivation and subsequent productivity ( Hancock, 2002 ; Henderlong and Lepper, 2002 ), factors highlighted by students as negatively affecting their depression. However, something to keep in mind is that a student with depression and a student without depression may process praise differently. For a student with depression, a small comment that praises the student’s work may not be sufficient for the student to process that comment as praise. People with depression are hyposensitive to reward or have reward-processing deficits ( Eshel and Roiser, 2010 ); therefore, praise may affect students without depression more positively than it would affect students with depression. Research mentors should be mindful that students with depression often have a negative view of themselves, and while students report that praise is extremely important, they may have trouble processing such positive feedback.

Normalize Failure and Be Explicit about the Importance of Research Contributions.

Explicitly remind students that experiencing failure is expected in research. Also explain to students how their individual work relates to the overall project so that they can understand how their contributions are important. It can also be helpful to explain to students why the research project as a whole is important in the context of the greater scientific community.

Experiencing failure has been thought to be a potentially important aspect of undergraduate research, because it may provide students with the potential to develop integral scientific skills such as the ability to navigate challenges and persevere ( Laursen et al. , 2010 ; Gin et al. , 2018 ; Henry et al. , 2019 ). However, in the interviews, students described that when their science experiments failed, it was particularly tough for their depression. Students’ negative reaction to experiencing failure in research is unsurprising, given recent literature that has predicted that students may be inadequately prepared to approach failure in science ( Henry et al. , 2019 ). However, the literature suggests that students with depression may find experiencing failure in research to be especially difficult ( Elliott et al. , 1997 ; Mongrain and Blackburn, 2005 ; Jones et al. , 2009 ). One potential hypothesis is that students with depression may be more likely to have fixed mindsets or more likely to believe that their intelligence and capacity for specific abilities are unchangeable traits ( Schleider and Weisz, 2018 ); students with a fixed mindset have been hypothesized to have particularly negative responses to experiencing failure in research, because they are prone to quitting easily in the face of challenges and becoming defensive when criticized ( Forsythe and Johnson, 2017 ; Dweck, 2008 ). A study of life sciences undergraduates enrolled in CUREs identified three strategies of students who adopted adaptive coping mechanisms, or mechanisms that help an individual maintain well-being and/or move beyond the stressor when faced with failure in undergraduate research: 1) problem solving or engaging in strategic planning and decision making, 2) support seeking or finding comfort and help with research, and 3) cognitive restructuring or reframing a problem from negative to positive and engaging in self encouragement ( Gin et al. , 2018 ). We recommend that, when undergraduates experience failure in science, their mentors be proactive in helping them problem solve, providing help and support, and encouraging them. Students also explained that mentors sharing their own struggles as undergraduate and graduate students was helpful, because it normalized failure. Sharing personal failures in research has been recommended as an important way to provide students with psychosocial support during research ( NASEM, 2019 ). We also suggest that research mentors take time to explain to students why their tasks in the lab, no matter how small, contribute to the greater research project ( Cooper et al. , 2019a ). Additionally, it is important to make sure that students can explain how the research project as a whole is contributing to the scientific community ( Gin et al. , 2018 ). Students highlighted that contributing to something important was really helpful for their depression, which is unsurprising, given that studies have shown that meaning in life or people’s comprehension of their life experiences along with a sense of overarching purpose one is working toward has been shown to be inversely related to depression ( Steger, 2013 ).

Limitations and Future Directions

This work was a qualitative interview study intended to document a previously unstudied phenomenon: depression in the context of undergraduate research experiences. We chose to conduct semistructured interviews rather than a survey because of the need for initial exploration of this area, given the paucity of prior research. A strength of this study is the sampling approach. We recruited a national sample of 35 undergraduates engaged in undergraduate research at 12 different public R1 institutions. Despite our representative sample from R1 institutions, these findings may not be generalizable to students at other types of institutions; lab environments, mentoring structures, and interactions between faculty and undergraduate researchers may be different at other institution types (e.g., private R1 institutions, R2 institutions, master’s-granting institutions, primarily undergraduate institutions, and community colleges), so we caution against making generalizations about this work to all undergraduate research experiences. Future work could assess whether students with depression at other types of institutions have similar experiences to students at research-intensive institutions. Additionally, we intentionally did not explore the experiences of students with specific identities owing to our sample size and the small number of students in any particular group (e.g., students of a particular race, students with a graduate mentor as the primary mentor). We intend to conduct future quantitative studies to further explore how students’ identities and aspects of their research affect their experiences with depression in undergraduate research.

The students who participated in the study volunteered to be interviewed about their depression; therefore, it is possible that depression is a more salient part of these students’ identities and/or that they are more comfortable talking about their depression than the average population of students with depression. It is also important to acknowledge the personal nature of the topic and that some students may not have fully shared their experiences ( Krumpal, 2013 ), particularly those experiences that may be emotional or traumatizing ( Kahn and Garrison, 2009 ). Additionally, our sample was skewed toward females (77%). While females do make up approximately 60% of students in biology programs on average ( Eddy et al. , 2014 ), they are also more likely to report experiencing depression ( American College Health Association, 2018 ; Evans et al. , 2018 ). However, this could be because women have higher rates of depression or because males are less likely to report having depression; clinical bias, or practitioners’ subconscious tendencies to overlook male distress, may underestimate depression rates in men ( Smith et al. , 2018 ). Further, females are also more likely to volunteer to participate in studies ( Porter and Whitcomb, 2005 ); therefore, many interview studies have disproportionately more females in the data set (e.g., Cooper et al. , 2017 ). If we had been able to interview more male students, we might have identified different findings. Additionally, we limited our sample to life sciences students engaged in undergraduate research at public R1 institutions. It is possible that students in other majors may have different challenges and opportunities for students with depression, as well as different disciplinary stigmas associated with mental health.

In this exploratory interview study, we identified a variety of ways in which depression in undergraduates negatively affected their undergraduate research experiences. Specifically, we found that depression interfered with students’ motivation and productivity, creativity and risk-taking, engagement and concentration, and self-perception and socializing. We also identified that research can negatively affect depression in undergraduates. Experiencing failure in research can exacerbate student depression, especially when students do not have access to adequate guidance. Additionally, being alone or having negative interactions with others in the lab worsened students’ depression. However, we also found that undergraduate research can positively affect students’ depression. Research can provide a familiar space where students can feel as though they are contributing to something meaningful. Additionally, students reported that having access to adequate guidance and a social support network within the research lab also positively affected their depression. We hope that this work can spark conversations about how to make undergraduate research experiences more inclusive of students with depression and that it can stimulate additional research that more broadly explores the experiences of undergraduate researchers with depression.

Important note

If you or a student experience symptoms of depression and want help, there are resources available to you. Many campuses provide counseling centers equipped to provide students, staff, and faculty with treatment for depression, as well as university-dedicated crisis hotlines. Additionally, there are free 24/7 services such as Crisis Text Line, which allows you to text a trained live crisis counselor (Text “CONNECT” to 741741; Text Depression Hotline , 2019 ), and phone hotlines such as the National Suicide Prevention Lifeline at 1-800-273-8255 (TALK). You can also learn more about depression and where to find help near you through the Anxiety and Depression Association of American website: https://adaa.org ( Anxiety and Depression Association of America, 2019 ) and the Depression and Biopolar Support Alliance: http://dbsalliance.org ( Depression and Biopolar Support Alliance, 2019 ).

ACKNOWLEDGMENTS

We are extremely grateful to the undergraduate researchers who shared their thoughts and experiences about depression with us. We acknowledge the ASU LEAP Scholars for helping us create the original survey and Rachel Scott for her helpful feedback on earlier drafts of this article. L.E.G. was supported by a National Science Foundation (NSF) Graduate Fellowship (DGE-1311230) and K.M.C. was partially supported by a Howard Hughes Medical Institute (HHMI) Inclusive Excellence grant (no. 11046) and an NSF grant (no. 1644236). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or HHMI.

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Submitted: 4 November 2019 Revised: 24 February 2020 Accepted: 6 March 2020

© 2020 K. M. Cooper, L. E. Gin, et al. CBE—Life Sciences Education © 2020 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Center for Collegiate Mental Health 2023 Annual Report

This report from the Center for Collegiate Mental Health (CCMH) presents descriptive data on various domains of mental health and factors related to treatment and care for college students in the United States. The data included in this report was gathered from 195 colleges and universities across the U.S., describing 185,114 de-identified college students seeking mental health treatment. The report examines trends among students seeking mental health services, including prior treatment and “threat-to-self" characteristics, along with trends in various mental health indicators, including depression, anxiety, family distress, and substance use. The report also includes data on appointment statistics, including utilization of mental health services and types of services.

The 2023 report also explores how experiences of discrimination are associated with mental health concerns at college counseling centers, analyzing how discrimination due to race/ethnicity, disability, gender, nationality, religion, or sexuality impact mental health.

Center for Collegiate Mental Health 2023 Annual Report. Center for Collegiate Mental Health 2024. https://ccmh.psu.edu/annual-reports .

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Open Access

Peer-reviewed

Research Article

Investigation of positive mental health levels among faculty of health sciences students at a rural university in South Africa

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Pharmacy, Faculty of Health Sciences, University of Limpopo, Mankweng, Limpopo Province, South Africa

Roles Formal analysis, Investigation, Project administration, Writing – original draft, Writing – review & editing

Roles Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

Roles Investigation, Writing – original draft, Writing – review & editing

Roles Investigation, Project administration, Writing – original draft, Writing – review & editing

Rajesh Vikram Vagiri,
Phuty Elizabeth Leboho,
Lokwene Katlego Desry,
Machaka Khutso,
Mbedzi Pfunzo

Published: July 29, 2024
https://doi.org/10.1371/journal.pgph.0002855
Peer Review
Reader Comments

One out of every four people in their lives can be affected by mental health problems that alter their functioning, behaviour, and thinking patterns. In recent years, there has been an increase in mental health disorders among students worldwide. Positive mental health (PMH) has gained relevance in today’s fast-paced and demanding world, especially for university students, as it affects their ability to learn, achieve academically, and behave appropriately. This study aimed to investigate the levels of PMH and identify the association between PMH domains and socio-demographic and health-related variables among Faculty of Health Sciences (FHS) students at a rural university in South Africa. A quantitative, descriptive, and cross-sectional survey was conducted. Data was collected using a multidimensional PMH instrument and a socio-demographic and health-related questionnaire, from 354 undergraduate students who are registered for various programmes offered by FHS. The data were analysed using IBM SPSS version 29. Most of the students were black (99.2%, n = 351), single (72%, n = 255), received a study bursary from the government (78.5%, n = 278), hailed from a rural area (77.7%, n = 275) and residing at the university campus (74.6%, n = 246). The total PMH scores of the participants ranged from 4.24 to 4.97 suggesting moderate to higher PMH levels. Significant differences in mean scores were observed in the total PMH and domains of PMH across various socio-demographic and health-related variables. Gender ( p = 0.037), age ( p = 0.043) and field of study ( p = 0.016) showed a significant association with total PMH score. The study’s findings highlighted the multi-dimensionality of mental health and justified the importance of evaluating the domains of PMH in university students. The disparities observed across different PMH domains underscore the necessity of embracing innovative approaches to achieve the most effective outcomes to improve mental health and the accurate management of symptoms in students.

Citation: Vagiri RV, Leboho PE, Desry LK, Khutso M, Pfunzo M (2024) Investigation of positive mental health levels among faculty of health sciences students at a rural university in South Africa. PLOS Glob Public Health 4(7): e0002855. https://doi.org/10.1371/journal.pgph.0002855

Editor: Medhin Selamu Tegegn, World Health Organization, ETHIOPIA

Received: November 13, 2023; Accepted: July 2, 2024; Published: July 29, 2024

Copyright: © 2024 Vagiri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Mental health is, to date, considered a neglected area in developing countries. Mental health problems can affect one out of every four people during their lives, by altering their functioning, behaviour, and thinking patterns [ 1 , 2 ]. In today’s fast-paced society, there is an increasing recognition of the importance of mental health and well-being [ 3 , 4 ]. Mental health refers to a state of emotional, psychological, and social well-being in which individuals can cope with the stresses of life, work productively, and make meaningful contributions to their communities [ 5 ]. Well-being has been proposed as a combination of two traditional approaches: “hedonic”- and “eudemonic” concepts. The hedonic concept of well-being has been defined as “high positive affect, low negative affect and high life satisfaction” which focuses on the feelings of individuals towards life. Whereas the eudemonic concept focuses on functioning in life, which includes the dimensions of self-acceptance, personal growth, autonomy, relationships, and environmental mastery [ 6 ].

In a fast-paced and demanding society, positive mental health (PMH) has become a topic of increasing importance, particularly among university students [ 6 , 7 ]. The concept of PMH goes beyond the absence of mental illness and encompasses the cultivation of emotional resilience, well-being, and overall psychological flourishing [ 4 , 8 , 9 ]. The concept of PMH was further supported by a study conducted by Keyes (2000) who described mental health as a ‘‘syndrome of symptoms of positive feelings and positive functioning in life”. A diagnosis of the presence of mental health, described as flourishing, and the absence of mental health, characterised as languishing. The study findings reported a risk of a major depressive episode that was two times more likely among languishing than moderately mentally healthy adults and nearly six times greater among languishing than flourishing adults [ 10 ].

Positive psychology in South Africa

The burden of mental disorders in South Africa has significantly increased over the past two decades, contributing substantially to the country’s overall disease burden [ 11 ]. This rise in mental health issues has led to a growing concern about the state of mental health services in South Africa, with evidence indicating the necessity for population-wide and individual-level interventions to enhance mental health literacy [ 12 ]. The Mental Health Policy Framework and Strategic Plan for South Africa (2013–2020) emphasised the importance of primary health care, multisector collaboration, and addressing issues such as stigma and gender in the country’s mental health policy framework [ 13 ].

The current state of positive psychology in South Africa is influenced by various factors, including the mental health policy framework, the treatment gap for mental disorders, the burden of mental disorders, the state of mental health services, and historical and contextual factors [ 14 ]. South Africa’s mental health policy framework underscores the need for integrated mental health care within primary health care, presenting both challenges and opportunities for the governance of the health system to support this integration [ 15 ]. Additionally, the experiences and perceptions of mental health service provision at primary health centres in South Africa indicate that mental healthcare remains sparse, while the impact of mental and substance use disorders continues to add to the burden of disease in African countries [ 16 ].

The stress-generating environment in South Africa has implications for the mental health of its population [ 17 ]. Apartheid has had a profound impact on South African psychology, with historical and contextual factors shaping the trajectory of psychological work on health issues [ 18 ]. Post-apartheid South Africa continues to grapple with endemic social problems that create conditions of oppression and violence, necessitating new ways of engagement in activism for improved mental health [ 13 ]. The challenges faced by South Africa, such as the HIV epidemic and the barriers to mental healthcare have further underscored the need for a comprehensive understanding of mental health in the country [ 19 , 20 ].

A study conducted by Coetzee and Viviers (2007) categorised South African research in positive psychology over the last 36 years, providing insights into the diversity of the fields of psychofortology and related disciplines represented by the published articles. It highlighted the lack of a cohesive foundational theoretical framework and the challenge of expanding a classification context to synthesise diverse states, traits, and outcomes for each other [ 21 ].

Differences in positive mental health between the urban and rural residents of South Africa

The socio-economic and cultural divide between urban and rural settings in South Africa significantly impacts human settlement and well-being. The environmental setting plays a major role in what one becomes, partly through determining the quality of education available, possible opportunities for formal employment, and quality of lifestyle [ 22 ]. Ataguba et al. (2011) highlighted the intertwining of human settlement with socio-economic factors, influencing living conditions and subjective well-being [ 22 ]. Rural areas in South Africa are characterised by poverty and underdevelopment related to the experience of psychological distress and ill health by rural residents. Individuals living in poverty experience lower levels of happiness, indicating the impact of socio-economic conditions on well-being [ 22 , 23 ]. Mthembu et al. (2017) observed an increase in psychological distress with rural residents, associated with poverty and underdevelopment, leading to psychological distress and ill health among residents [ 24 ].

Although literature exists on the impact of urban living on physical health and mental illness, there is very little on psychosocial well-being. A study conducted in the North West Province of South Africa explored the socio-demographic variables and psychosocial well-being of an African group in rural and urban areas using the General Psychological Well-being (GPW) and the Mental Health Continuum (MHC) models. The study findings revealed that urban participants reported higher levels of psychosocial well-being in most facets of individual and social well-being compared to rural residents. The above findings further suggest that the current state of African rural life is detrimental to well-being [ 23 ].

Positive mental health of university students in South Africa

Globally, there has been a rise in mental disorders among students in recent years. According to the WHO World Mental Health International College Student Project conducted at 19 colleges in eight developed and developing countries, that colleges are contending with increased rates of mental health conditions. Students reported a combination of mental disorders such as major depression, mania, anxiety disorders, panic disorder, alcohol use disorder, and substance use disorder that impacted their academic performance and overall well-being [ 25 ]. In response to this alarming trend, it is crucial for higher education institutions to actively address the mental health needs of their students by creating a healthier campus environment that supports PMH and well-being [ 26 – 28 ].

Additionally, there is a growing recognition of the need for preventive mental health practices among university students [ 6 , 7 ]. As students navigate the pressures of academics, social interactions, and personal development, it is crucial to understand and support their PMH [ 29 , 30 ]. Understanding and promoting PMH among university students is vital to ensuring their holistic development and academic success [ 31 ]. Furthermore, a systematic review by Hobbs et al. (2022) highlighted the importance of addressing poor psychological well-being in university students, as it can impair academic performance and increase the likelihood of dropping out. The study recommended rigorous evaluation of positive psychology and the implementation of interventions to promote students’ psychological well-being [ 32 ].

In recent years, few studies have been conducted in South Africa to assess mental well-being, focusing on the investigation of the prevalence of mental health symptoms or conditions among university students based in urban areas. Bantjes et al. (2019) conducted a study on the prevalence and socio-demographic correlates of common mental disorders (CMDs) among first-year university students in post-apartheid South Africa [ 33 ]. Another study investigated the prevalence and factors associated with mental distress among university students in the Eastern Cape Province, South Africa [ 34 ]. Both studies highlighted the need for targeted interventions to address mental health issues and support the mental well-being of university students.

On the other hand, few studies have explored the influence of COVID-19-related experiences on the mental health of South African university students. This research sheds light on the impact of the COVID-19 pandemic on the mental health of university students, providing valuable insights and a deeper understanding of the challenges faced by this population during the pandemic [ 35 , 36 ].

The above studies investigated the mental well-being of the student population in South Africa and provided comprehensive insights into the mental health challenges faced by South African university students, including the prevalence of mental disorders, the impact of stressful life events, and the influence of the COVID-19 pandemic on mental well-being. However, none of the studies assessed various domains of PMH among university students in South Africa, especially in a rural setting. A PMH for students is very important as they navigate the challenges of higher education and face various stressors such as academic pressure, financial burdens, and social expectations. These stressors can have a significant impact on their overall well-being and academic performance [ 37 – 39 ]. Therefore, this study aimed to investigate the levels of PMH and its association with socio-demographic and health-related variables among FHS students at a rural university in South Africa.

Study design and population

A cross-sectional survey was conducted among Faculty of Health Sciences (FHS) students at a rural university in South Africa. A cross-sectional research design was used because it allows studies to collect data to make inferences about a population of interest at one point in time. This study followed a descriptive and quantitative approach that included FHS undergraduate students who were registered in the fields of medicine, medical sciences, pharmacy, optometry, nursing sciences, and human nutrition and dietetics (HND).

A biostatistician from the university was consulted to estimate the sample size. The sample size was calculated using Slovin’s formula.

n = N / (1+Ne 2 )

n = study sample size; N = total number of registered students; and e = margin of error

n = 1679/ (1+1679 X 0.05 2 )

n = 322 students

A 10% attrition rate was added to the calculated sample size to cover for missing or incomplete data, resulting in a total sample of 354 students. The sample was proportionally distributed between various fields of study: Pharmacy (n = 61), Optometry (n = 40), Human Nutrition & Dietetics (HND) (n = 49), Medicine (n = 92), Medical Sciences (n = 52) and Nursing Sciences (n = 60) to achieve the desired sample.

Data collection tools

All the data collection instruments, study information leaflets, and consent forms were available only in English, as the medium of instruction and communication at the study site is English. All the data collection instruments were self-administered.

Socio-demographic and health-related questionnaire.

The socio-demographic and health-related information, such as gender, race, age, field of study, religious affiliation, household income, family residence, relationship status, history of psychiatric illness, whether they have taken any medicine for treating psychiatric illness, and who they are currently living with was obtained from the participants.

Positive mental health instrument.

The researchers used a multi-dimensional PMH instrument developed by Vaingankar et al., (2011) [ 40 ]. The PMH instrument is a self-administered tool that covers all key and culturally appropriate domains of mental health and can be applied to compare levels of mental health across different populations. This instrument was developed through qualitative investigations of people with mental disorders, followed by quantitative and psychometric analysis. The research team obtained authorisation from the authors of the PMH instrument to use it in this study. This includes a copyright grant and the scoring algorithm.

This instrument has 57 questions, which include six domains:

The domains include:

General coping refers to individuals’ responses and coping strategies during stressful situations and their ability to think positively and participate in selected activities.
Emotional support is key for helping people cope with difficult situations in life and for making them feel loved and wanted. A willingness to share the burden with others is important to obtain compassionate advice and care.
Spirituality encompasses both spiritual and religious practices and beliefs that influence individual’s faith and behaviour in life. This contributes to PMH as a coping mechanism and means of establishing strong social support and networks.
Interpersonal skills are associated with all aspects of mental health and are essential in helping the individual develop and maintain good relationships, which in turn provide the support and network needed during times of distress.
Personal Growth and Autonomy mean knowing one’s goals and ways to achieve them, which is a sign of good mental health. It reflects the level of confidence, freedom, sense of purpose, and the ability to self-evaluate and make decisions.
Global Affect is the experience of a positive mood, which is a sign of mental health. Calm, happiness, and enthusiasm mean emotional stability and are full of energy.

For the first five domains, participants were requested to mark how much each item describes them on a scale between 1 to 6 (1- ‘Not at all like me’ to 6-’Exactly like me’). For the ‘Global affect’ domain, they were requested to indicate ‘how often over the past four weeks they felt calm, peaceful, relaxed, and enthusiastic’ using a 5-point response scale (1- ‘Never or very rarely’ to 5- ‘Very often or always’). Domain-specific scores were calculated by summing the scores of the respective items and dividing by the total number of items in each domain.

Data collection

Data were collected over three weeks, from September 2023 to October 2023, by the research team. The research team designed a study information leaflet that provided detailed information about the study, possible benefits, and implications. The researchers were trained on the data collection instruments, study information leaflet, and consent form to clarify and address the concerns raised by the participants. The students who met the inclusion criteria were conveniently enrolled in the study. The following inclusion criteria were applied: registered undergraduate students of FHS from the first to the final level, who are older than 18 years and who agreed to participate and sign the consent form. Students from the Department of Public Health (undergraduate programme not offered), postgraduate students, and students who declined to participate were excluded from the study.

On the day of data collection, the researchers briefed the participants about the study after the conclusion of their classes, explained the purpose and importance of this study, and requested their participation. The questionnaires were administered only after the participants had given written consent to participate in the study. The participants were given sufficient time to complete the questionnaires.

Validity and reliability

Globally available instruments focused on specific domains of mental health in greater detail, using lengthy or too short questionnaires to provide significant comparisons and detection of changes across the domains. However, the PMH instrument utilised in this study comprehensively evaluated the multi-dimensional domains of mental health, which is appropriate for a South African study. The PMH instrument is a cross-cultural questionnaire developed by Vaingankar et al., (2011) and further validated by studies conducted in Singapore and South Africa [ 41 , 42 ].

To ensure the validity of the data in this study, the researchers recruited the sample, which was representative of the target population, thus increasing the external validity and generalisability of the findings. Rigorous data collection methods were employed by the researchers in this study. Only the research team was involved in briefing the participants and during data collection to maintain consistency with data collection procedures. The researchers implemented data validation procedures by double-checking the thoroughness and completeness of the data. Data entry was verified for the correctness and completeness prior to data analysis to enhance reliability. The inclusion and exclusion criteria set for this study were strictly applied.

Selection bias was minimised by strictly applying the inclusion and exclusion criteria. All the data collection instruments were self-administered, avoiding interviewer bias. The introduction of translation bias was excluded as all the questionnaires and consent forms were available only in English.

Data analysis

A biostatistician from the university was consulted for assistance and advise on the data analysis. Responses from the questionnaires were exported to Microsoft Office Excel and analysed using the IBM Statistical Package for Social Studies (SPSS) version 29. The PMH scores were calculated using the scoring script provided by the authors of the PMH instrument. Recorded item scores for the questions specific to each domain were pooled together and divided by a number of questions to obtain the domain scores. The PMH scores ranged from 1 (lower PMH) to 6 (higher PMH). The statistical significance for the study was set at p≤0.05 using 2-sided tests. Independent t-tests and Analysis of Variance (ANOVA) were used to establish differences in mean scores between Total PMH and specific domains and socio-demographic and health-related variables.

Ethical considerations

Ethical clearance (TREC/518/2023:UG) to conduct the study was obtained from the university’s research ethics committee. Permission to conduct the study was obtained from the Registrar of the university. Authorisation to use the PMH instrument was obtained from the National Institute of Mental Health, Singapore.

A participant information leaflet was provided to the students, informing them of the objectives of the study, that participation in the study is voluntary, that they have the right to withdraw from the study at any time without providing reasons, and that their identity will remain anonymous. All the concerns of the prospective participants about the study were clarified before the commencement of data collection. All the participants have provided written consent before participating in the study. The study adhered to all ethical standards and ethical research practices, following the Declaration of Helsinki. All the records and data were kept in a safe and secure place to maintain confidentiality, with access available only to the research team.

The study population comprised 354 FHS students from the first year to the final year with a mean age of 20.6 years. Table 1 displays the socio-demographic and health-related characteristics of the respondents. The majority of the students were black (99.2%, n = 351), single (72%, n = 255), received study bursaries from the government (78.5%, n = 278), hail from a rural area (77.7%, n = 275) and resided in the university campus (74.5%, n = 246). More than half of the students were female (59.9%), affiliated with Christianity (69.2%), and had a household income of less than 10 000 South African Rand (ZAR) per month (50.4%, n = 182). Most of the students indicated that they did not have a history of any psychiatric illness (96%, n = 340) or had taken any psychiatric medicines before (96.3%, n = 341).

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https://doi.org/10.1371/journal.pgph.0002855.t001

Total PMH and domain-specific scores by socio-demographic and health-related variables

Table 2 displays the mean total PMH and domain-specific scores by socio-demographic and health-related variables. The total PMH scores ranged from 4.24 to 4.97 suggesting that the students of FHS reported a moderate to higher level of PMH. Significant differences were observed in the total PMH and domain mean scores across various socio-demographic and health-related variables. Gender ( p = 0.037), age ( p = 0.043) and field of study ( p = 0.016) had a significant influence on total PMH score. In the general coping domain, significant differences in mean scores were observed with gender ( p <0.001), where males reporting significantly higher scores as compared to females. Significant differences in mean scores regarding the emotional support domain were observed with race ( p = 0.039) and relationship status ( p = 0.005). There is a significant difference in mean scores between the field of study ( p = 0.043), gender ( p = 0.008) and religious affiliation ( p = 0.009) on spirituality. In the case of the interpersonal skills domain, a significant difference in mean scores was observed between the field of study ( p = 0.025) and current living status ( p = 0.008). With respect to personal growth of autonomy, significant differences in mean scores were observed in age ( p = 0.022), field of study ( p = 0.002), history of psychiatric illness ( p = 0.002) and gender ( p = 0.003). In the global affect, a significant difference in mean score was observed with age ( p <0.001) and gender ( p <0.001).

https://doi.org/10.1371/journal.pgph.0002855.t002

It is evident from the study findings that most of the students that participated in this study were black, from a rural background, with low household income, and stayed at the university residence. This can be attributed to the location of the university, which is in the rural and poorest part of South Africa with a predominantly black population. The university was established during apartheid to provide education to blacks [ 43 , 44 ]. The university continues to be a first choice for students from the disadvantaged communities in the province due to its proximity to their homes [ 45 ].

Our findings showed significant differences in the levels of the total PMH between men and women. However, most of the students with a low level of PMH were female, which aligns with the findings of previous research [ 46 , 47 ]. For college students, stress is a major problem, and they regard their college years as among the most stressful times of their lives. Male students have better coping skills with stress as they become more proactive in their stress response. It has also been demonstrated that female students experience higher levels of general and academic stress than their male counterparts [ 48 ] which could have influenced their total PMH levels. However, other studies did not identify any total perceived stress differences in their college populations, which contrasts with our study findings [ 49 , 50 ]. As illustrated, the existing research has produced conflicting results about the relationship between gender and perceived stress levels, which calls for further investigation. The possible explanation for significantly low levels of total PMH in female students could also be linked to menstruation, as there was an association between menstrual-related symptoms and the levels of psychological distress [ 51 ]. The above finding highlights the need for South African universities to implement gender-specific interventional programmes that address gender-specific risk factors.

Age was found to be significantly associated with total PMH, with students between the ages of 18 and 19 years (first year of study) reporting significantly lower mean scores compared to other age groups in most domains, excluding emotional support and interpersonal skills. Our study finding is supported by studies conducted by WHO and Dessie et al. (2013) in Ethiopia, which found that students in the first year of study experienced a greater variety of stressors than any other group of students [ 25 , 52 ]. The finding may be attributed to the fact that first-year students are in their transition to university life, and the added pressure of heightened social and academic expectations puts them at risk for mental health issues. In addition, first year students’ inability to navigate their way and cope with the stress of such a transition, loneliness, and homesickness could have significantly influenced their PMH levels [ 34 , 53 ]. Results from a study conducted among Malaysian university students revealed that factors associated with financial difficulties, demands of the university environment and the university’s administrative processes, and non-academic related issues were reported as underlying factors in first-year students’ low levels of mental well-being [ 54 ]. A study conducted by Mason (2019) with a sample of 55 first-year university students in South Africa highlighted the need for more South African research on the application of positive psychology to assist students in navigating the stressful first-year experience by identifying, developing, and applying signature strengths [ 55 ].

In this study, the field of study was found to be significantly associated with total PMH, with students in HND reporting higher mean scores in most domains except general coping and emotional support when compared to the students from other fields of study. Although there is no specific reason to link their significantly higher levels of total PMH, the authors assume that their curriculum is less intense as compared to other undergraduate degrees offered by FHS.

This study identified a significant association between relationship status and emotional support. Students who were either in a relationship or married reported significantly higher scores compared to students who were single in the emotional support domain. Over the course of a person’s life, being single consistently correlates with poor mental health, in contrast to marriage. Literature further suggests that, compared to marriage, being single is one of the risk factors for depressive symptoms in both men and women. Our study finding is supported by existing literature that being single is associated with poorer levels of PMH in younger people, indicating that living circumstances or legal status are not as significant at this age [ 56 , 57 ].

Limitations

This is a cross-sectional study conducted with a comparatively smaller group of students in FHS at a rural university in South Africa. Therefore, we cannot generalise the results to the entire student population in South Africa. Therefore, this study may not have established causal relationships between variables and may have only provided information on associations.

The authors acknowledge the possibility of response bias in this study as some students may have biases or motivations that influence their responses, which may have led to inaccurate or skewed data. As this study was conducted at a specific institution and in a geographical area of South Africa that has a predominant black population, it excluded students from other races. Hence, the researchers cannot justify the effect of race on total PMH and other domains of PMH. Despite the fact that students with a history of psychiatric illness and those receiving treatment reported lower levels of PMH, the limited sample makes the findings unjustifiable. Another limitation of this study is social desirability, as some students may have responded in a socially desirable way, which may have yielded inaccurate or biassed responses.

Although these limitations exist, the researchers believe that the results of this study are robust, convincing, and provide a platform for future research to further investigate the PMH levels in various university students with a larger study population and compare the levels of PMH in urban and rural universities in South Africa.

This is the first study in South Africa to measure the levels of PMH among the FHS students at a rural university in South Africa using a multi-dimensional PMH instrument. The study findings clearly suggest that there is a place for gender-specific interventional programmes addressing gender-specific risk factors in South African universities. Interventions must be aimed at providing a platform for educating students regarding issues, such as mental distress, that are typically encountered during the first year of study, and the importance of developing a range of effective social supports. Higher education institutions should make student mental health and well-being a priority issue, as it directly influences the learning process and adjustment to the academic environment. By creating a culture of well-being and providing resources and support services, universities can contribute to positive mental health outcomes for their students.

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Student mental health is in crisis. Campuses are rethinking their approach

Amid massive increases in demand for care, psychologists are helping colleges and universities embrace a broader culture of well-being and better equipping faculty to support students in need

Vol. 53 No. 7 Print version: page 60

Mental Health

college student looking distressed while clutching textbooks

By nearly every metric, student mental health is worsening. During the 2020–2021 school year, more than 60% of college students met the criteria for at least one mental health problem, according to the Healthy Minds Study, which collects data from 373 campuses nationwide ( Lipson, S. K., et al., Journal of Affective Disorders , Vol. 306, 2022 ). In another national survey, almost three quarters of students reported moderate or severe psychological distress ( National College Health Assessment , American College Health Association, 2021).

Even before the pandemic, schools were facing a surge in demand for care that far outpaced capacity, and it has become increasingly clear that the traditional counseling center model is ill-equipped to solve the problem.

“Counseling centers have seen extraordinary increases in demand over the past decade,” said Michael Gerard Mason, PhD, associate dean of African American Affairs at the University of Virginia (UVA) and a longtime college counselor. “[At UVA], our counseling staff has almost tripled in size, but even if we continue hiring, I don’t think we could ever staff our way out of this challenge.”

Some of the reasons for that increase are positive. Compared with past generations, more students on campus today have accessed mental health treatment before college, suggesting that higher education is now an option for a larger segment of society, said Micky Sharma, PsyD, who directs student life’s counseling and consultation service at The Ohio State University (OSU). Stigma around mental health issues also continues to drop, leading more people to seek help instead of suffering in silence.

But college students today are also juggling a dizzying array of challenges, from coursework, relationships, and adjustment to campus life to economic strain, social injustice, mass violence, and various forms of loss related to Covid -19.

As a result, school leaders are starting to think outside the box about how to help. Institutions across the country are embracing approaches such as group therapy, peer counseling, and telehealth. They’re also better equipping faculty and staff to spot—and support—students in distress, and rethinking how to respond when a crisis occurs. And many schools are finding ways to incorporate a broader culture of wellness into their policies, systems, and day-to-day campus life.

“This increase in demand has challenged institutions to think holistically and take a multifaceted approach to supporting students,” said Kevin Shollenberger, the vice provost for student health and well-being at Johns Hopkins University. “It really has to be everyone’s responsibility at the university to create a culture of well-being.”

Higher caseloads, creative solutions

The number of students seeking help at campus counseling centers increased almost 40% between 2009 and 2015 and continued to rise until the pandemic began, according to data from Penn State University’s Center for Collegiate Mental Health (CCMH), a research-practice network of more than 700 college and university counseling centers ( CCMH Annual Report , 2015 ).

That rising demand hasn’t been matched by a corresponding rise in funding, which has led to higher caseloads. Nationwide, the average annual caseload for a typical full-time college counselor is about 120 students, with some centers averaging more than 300 students per counselor ( CCMH Annual Report , 2021 ).

“We find that high-caseload centers tend to provide less care to students experiencing a wide range of problems, including those with safety concerns and critical issues—such as suicidality and trauma—that are often prioritized by institutions,” said psychologist Brett Scofield, PhD, executive director of CCMH.

To minimize students slipping through the cracks, schools are dedicating more resources to rapid access and assessment, where students can walk in for a same-day intake or single counseling session, rather than languishing on a waitlist for weeks or months. Following an evaluation, many schools employ a stepped-care model, where the students who are most in need receive the most intensive care.

Given the wide range of concerns students are facing, experts say this approach makes more sense than offering traditional therapy to everyone.

“Early on, it was just about more, more, more clinicians,” said counseling psychologist Carla McCowan, PhD, director of the counseling center at the University of Illinois at Urbana-Champaign. “In the past few years, more centers are thinking creatively about how to meet the demand. Not every student needs individual therapy, but many need opportunities to increase their resilience, build new skills, and connect with one another.”

Students who are struggling with academic demands, for instance, may benefit from workshops on stress, sleep, time management, and goal-setting. Those who are mourning the loss of a typical college experience because of the pandemic—or facing adjustment issues such as loneliness, low self-esteem, or interpersonal conflict—are good candidates for peer counseling. Meanwhile, students with more acute concerns, including disordered eating, trauma following a sexual assault, or depression, can still access one-on-one sessions with professional counselors.

As they move away from a sole reliance on individual therapy, schools are also working to shift the narrative about what mental health care on campus looks like. Scofield said it’s crucial to manage expectations among students and their families, ideally shortly after (or even before) enrollment. For example, most counseling centers won’t be able to offer unlimited weekly sessions throughout a student’s college career—and those who require that level of support will likely be better served with a referral to a community provider.

“We really want to encourage institutions to be transparent about the services they can realistically provide based on the current staffing levels at a counseling center,” Scofield said.

The first line of defense

Faculty may be hired to teach, but schools are also starting to rely on them as “first responders” who can help identify students in distress, said psychologist Hideko Sera, PsyD, director of the Office of Equity, Inclusion, and Belonging at Morehouse College, a historically Black men’s college in Atlanta. During the pandemic, that trend accelerated.

“Throughout the remote learning phase of the pandemic, faculty really became students’ main points of contact with the university,” said Bridgette Hard, PhD, an associate professor and director of undergraduate studies in psychology and neuroscience at Duke University. “It became more important than ever for faculty to be able to detect when a student might be struggling.”

Many felt ill-equipped to do so, though, with some wondering if it was even in their scope of practice to approach students about their mental health without specialized training, Mason said.

Schools are using several approaches to clarify expectations of faculty and give them tools to help. About 900 faculty and staff at the University of North Carolina have received training in Mental Health First Aid , which provides basic skills for supporting people with mental health and substance use issues. Other institutions are offering workshops and materials that teach faculty to “recognize, respond, and refer,” including Penn State’s Red Folder campaign .

Faculty are taught that a sudden change in behavior—including a drop in attendance, failure to submit assignments, or a disheveled appearance—may indicate that a student is struggling. Staff across campus, including athletic coaches and academic advisers, can also monitor students for signs of distress. (At Penn State, eating disorder referrals can even come from staff working in food service, said counseling psychologist Natalie Hernandez DePalma, PhD, senior director of the school’s counseling and psychological services.) Responding can be as simple as reaching out and asking if everything is going OK.

Referral options vary but may include directing a student to a wellness seminar or calling the counseling center to make an appointment, which can help students access services that they may be less likely to seek on their own, Hernandez DePalma said. Many schools also offer reporting systems, such as DukeReach at Duke University , that allow anyone on campus to express concern about a student if they are unsure how to respond. Trained care providers can then follow up with a welfare check or offer other forms of support.

“Faculty aren’t expected to be counselors, just to show a sense of care that they notice something might be going on, and to know where to refer students,” Shollenberger said.

At Johns Hopkins, he and his team have also worked with faculty on ways to discuss difficult world events during class after hearing from students that it felt jarring when major incidents such as George Floyd’s murder or the war in Ukraine went unacknowledged during class.

Many schools also support faculty by embedding counselors within academic units, where they are more visible to students and can develop cultural expertise (the needs of students studying engineering may differ somewhat from those in fine arts, for instance).

When it comes to course policy, even small changes can make a big difference for students, said Diana Brecher, PhD, a clinical psychologist and scholar-in-residence for positive psychology at Toronto Metropolitan University (TMU), formerly Ryerson University. For example, instructors might allow students a 7-day window to submit assignments, giving them agency to coordinate with other coursework and obligations. Setting deadlines in the late afternoon or early evening, as opposed to at midnight, can also help promote student wellness.

At Moraine Valley Community College (MVCC) near Chicago, Shelita Shaw, an assistant professor of communications, devised new class policies and assignments when she noticed students struggling with mental health and motivation. Those included mental health days, mindful journaling, and a trip with family and friends to a Chicago landmark, such as Millennium Park or Navy Pier—where many MVCC students had never been.

Faculty in the psychology department may have a unique opportunity to leverage insights from their own discipline to improve student well-being. Hard, who teaches introductory psychology at Duke, weaves in messages about how students can apply research insights on emotion regulation, learning and memory, and a positive “stress mindset” to their lives ( Crum, A. J., et al., Anxiety, Stress, & Coping , Vol. 30, No. 4, 2017 ).

Along with her colleague Deena Kara Shaffer, PhD, Brecher cocreated TMU’s Thriving in Action curriculum, which is delivered through a 10-week in-person workshop series and via a for-credit elective course. The material is also freely available for students to explore online . The for-credit course includes lectures on gratitude, attention, healthy habits, and other topics informed by psychological research that are intended to set students up for success in studying, relationships, and campus life.

“We try to embed a healthy approach to studying in the way we teach the class,” Brecher said. “For example, we shift activities every 20 minutes or so to help students sustain attention and stamina throughout the lesson.”

Creative approaches to support

Given the crucial role of social connection in maintaining and restoring mental health, many schools have invested in group therapy. Groups can help students work through challenges such as social anxiety, eating disorders, sexual assault, racial trauma, grief and loss, chronic illness, and more—with the support of professional counselors and peers. Some cater to specific populations, including those who tend to engage less with traditional counseling services. At Florida Gulf Coast University (FGCU), for example, the “Bold Eagles” support group welcomes men who are exploring their emotions and gender roles.

The widespread popularity of group therapy highlights the decrease in stigma around mental health services on college campuses, said Jon Brunner, PhD, the senior director of counseling and wellness services at FGCU. At smaller schools, creating peer support groups that feel anonymous may be more challenging, but providing clear guidelines about group participation, including confidentiality, can help put students at ease, Brunner said.

Less formal groups, sometimes called “counselor chats,” meet in public spaces around campus and can be especially helpful for reaching underserved groups—such as international students, first-generation college students, and students of color—who may be less likely to seek services at a counseling center. At Johns Hopkins, a thriving international student support group holds weekly meetings in a café next to the library. Counselors typically facilitate such meetings, often through partnerships with campus centers or groups that support specific populations, such as LGBTQ students or student athletes.

“It’s important for students to see counselors out and about, engaging with the campus community,” McCowan said. “Otherwise, you’re only seeing the students who are comfortable coming in the door.”

Peer counseling is another means of leveraging social connectedness to help students stay well. At UVA, Mason and his colleagues found that about 75% of students reached out to a peer first when they were in distress, while only about 11% contacted faculty, staff, or administrators.

“What we started to understand was that in many ways, the people who had the least capacity to provide a professional level of help were the ones most likely to provide it,” he said.

Project Rise , a peer counseling service created by and for Black students at UVA, was one antidote to this. Mason also helped launch a two-part course, “Hoos Helping Hoos,” (a nod to UVA’s unofficial nickname, the Wahoos) to train students across the university on empathy, mentoring, and active listening skills.

At Washington University in St. Louis, Uncle Joe’s Peer Counseling and Resource Center offers confidential one-on-one sessions, in person and over the phone, to help fellow students manage anxiety, depression, academic stress, and other campus-life issues. Their peer counselors each receive more than 100 hours of training, including everything from basic counseling skills to handling suicidality.

Uncle Joe’s codirectors, Colleen Avila and Ruchika Kamojjala, say the service is popular because it’s run by students and doesn’t require a long-term investment the way traditional psychotherapy does.

“We can form a connection, but it doesn’t have to feel like a commitment,” said Avila, a senior studying studio art and philosophy-neuroscience-psychology. “It’s completely anonymous, one time per issue, and it’s there whenever you feel like you need it.”

As part of the shift toward rapid access, many schools also offer “Let’s Talk” programs , which allow students to drop in for an informal one-on-one session with a counselor. Some also contract with telehealth platforms, such as WellTrack and SilverCloud, to ensure that services are available whenever students need them. A range of additional resources—including sleep seminars, stress management workshops, wellness coaching, and free subscriptions to Calm, Headspace, and other apps—are also becoming increasingly available to students.

Those approaches can address many student concerns, but institutions also need to be prepared to aid students during a mental health crisis, and some are rethinking how best to do so. Penn State offers a crisis line, available anytime, staffed with counselors ready to talk or deploy on an active rescue. Johns Hopkins is piloting a behavioral health crisis support program, similar to one used by the New York City Police Department, that dispatches trained crisis clinicians alongside public safety officers to conduct wellness checks.

A culture of wellness

With mental health resources no longer confined to the counseling center, schools need a way to connect students to a range of available services. At OSU, Sharma was part of a group of students, staff, and administrators who visited Apple Park in Cupertino, California, to develop the Ohio State: Wellness App .

Students can use the app to create their own “wellness plan” and access timely content, such as advice for managing stress during final exams. They can also connect with friends to share articles and set goals—for instance, challenging a friend to attend two yoga classes every week for a month. OSU’s apps had more than 240,000 users last year.

At Johns Hopkins, administrators are exploring how to adapt school policies and procedures to better support student wellness, Shollenberger said. For example, they adapted their leave policy—including how refunds, grades, and health insurance are handled—so that students can take time off with fewer barriers. The university also launched an educational campaign this fall to help international students navigate student health insurance plans after noticing below average use by that group.

Students are a key part of the effort to improve mental health care, including at the systemic level. At Morehouse College, Sera serves as the adviser for Chill , a student-led advocacy and allyship organization that includes members from Spelman College and Clark Atlanta University, two other HBCUs in the area. The group, which received training on federal advocacy from APA’s Advocacy Office earlier this year, aims to lobby public officials—including U.S. Senator Raphael Warnock, a Morehouse College alumnus—to increase mental health resources for students of color.

“This work is very aligned with the spirit of HBCUs, which are often the ones raising voices at the national level to advocate for the betterment of Black and Brown communities,” Sera said.

Despite the creative approaches that students, faculty, staff, and administrators are employing, students continue to struggle, and most of those doing this work agree that more support is still urgently needed.

“The work we do is important, but it can also be exhausting,” said Kamojjala, of Uncle Joe’s peer counseling, which operates on a volunteer basis. “Students just need more support, and this work won’t be sustainable in the long run if that doesn’t arrive.”

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Online Mental Health Help Seeking Behaviors Among College Students

Rachel Johnson Kennesaw State University https://orcid.org/0000-0001-8955-8980
Dr. Monica Nandan Kennesaw State University https://orcid.org/0000-0002-0952-7963
Dr. Brian Culp Kennesaw State University https://orcid.org/0000-0001-5587-1979
Dr. Dominic Thomas Kennesaw State University https://orcid.org/0000-0001-5369-2467

Even before the pandemic, utilization of online mental health resources continues to grow among young adults. There is limited research on online help seeking behaviors, let alone specific research on college students’ online mental health help seeking behaviors. This study aims to identify which terminology college students utilize on online search engines to seek assistance related to mental health. A cross-sectional survey design was used during the 2019-2020 academic year. The respondents consisted of 259 college students at one of the 50 largest public institutions of higher education in the Southeastern United States.Overall, the sample data suggests that college students utilize general words such as mental health help, as well as specific symptoms and diagnoses, and words related to cost and insurance when searching online for mental health assistance. This data suggest online search word utilization for mental health has gender, racial, and age differences, which could further inform the way online search engines operate to provide adequate services for specific populations seeking help.

Author Biographies

Dr. monica nandan, kennesaw state university.

Department of Social Work and Human Services

Dr. Brian Culp, Kennesaw State University

Department of Health Promotion and Physical Education

Dr. Dominic Thomas, Kennesaw State University

Department of Information Systems

How to Cite

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License .

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Published: 16 August 2024

Examining the perception of undergraduate health professional students of their learning environment, learning experience and professional identity development: a mixed-methods study

Banan Mukhalalati 1 ,
Aaliah Aly 1 ,
Ola Yakti 1 ,
Sara Elshami 1 ,
Alaa Daud 2 ,
Ahmed Awaisu 1 ,
Ahsan Sethi 3 ,
Alla El-Awaisi 1 ,
Derek Stewart 1 ,
Marwan Farouk Abu-Hijleh 4 &
Zubin Austin 5

BMC Medical Education volume 24 , Article number: 886 ( 2024 ) Cite this article

Metrics details

The quality of the learning environment significantly impacts student engagement and professional identity formation in health professions education. Despite global recognition of its importance, research on student perceptions of learning environments across different health education programs is scarce. This study aimed to explore how health professional students perceive their learning environment and its influence on their professional identity development.

An explanatory mixed-methods approach was employed. In the quantitative phase, the Dundee Ready Education Environment Measure [Minimum–Maximum possible scores = 0–200] and Macleod Clark Professional Identity Scale [Minimum–Maximum possible scores = 1–45] were administered to Qatar University-Health students ( N = 908), with a minimum required sample size of 271 students. Data were analyzed using SPSS, including descriptive statistics and inferential analysis. In the qualitative phase, seven focus groups (FGs) were conducted online via Microsoft Teams. FGs were guided by a topic guide developed from the quantitative results and the framework proposed by Gruppen et al. (Acad Med 94:969-74, 2019), transcribed verbatim, and thematically analyzed using NVIVO®.

The questionnaire response rate was 57.8% (525 responses out of 908), with a usability rate of 74.3% (390 responses out of 525) after excluding students who only completed the demographic section. The study indicated a “more positive than negative” perception of the learning environment (Median [IQR] = 132 [116–174], Minimum–Maximum obtained scores = 43–185), and a “good” perception of their professional identity (Median [IQR] = 24 [22–27], Minimum–Maximum obtained scores = 3–36). Qualitative data confirmed that the learning environment was supportive in developing competence, interpersonal skills, and professional identity, though opinions on emotional support adequacy were mixed. Key attributes of an ideal learning environment included mentorship programs, a reward system, and measures to address fatigue and boredom.

Conclusions

The learning environment at QU-Health was effective in developing competence and interpersonal skills. Students' perceptions of their learning environment positively correlated with their professional identity. Ideal environments should include mentorship programs, a reward system, and strategies to address fatigue and boredom, emphasizing the need for ongoing improvements in learning environments to enhance student satisfaction, professional identity development, and high-quality patient care.

Peer Review reports

The learning environment is fundamental to higher education and has a profound impact on student outcomes. As conceptualized by Gruppen et al. [ 1 ], it comprises a complex interplay of physical, social, and virtual factors that shape student engagement, perception, and overall development. Over the last decade, there has been a growing global emphasis on the quality of the learning environment in higher education [ 2 , 3 , 4 ]. This focus stems from the recognition that a well-designed learning environment that includes good facilities, effective teaching methods, strong social interactions, and adherence to cultural and administrative standards can greatly improve student development [ 2 , 5 , 6 , 7 ]. Learning environments impact not only knowledge acquisition and skill development but also value formation and the cultivation of professional attitudes [ 5 ].

Professional identity is defined as the “attitudes, values, knowledge, beliefs, and skills shared with others within a professional group” [ 8 ]. The existing research identified a significant positive association between the development of professional identity and the quality of the learning environment, and this association is characterized by being multifaceted and dynamic [ 9 ]. According to Hendelman and Byszewski [ 10 ] a supportive learning environment, characterized by positive role models, effective feedback mechanisms, and opportunities for reflective practice, fosters the development of a strong professional identity among medical students. Similarly, Jarvis-Selinger et al. [ 11 ] argue that a nurturing learning environment facilitates the socialization process which enables students to adopt and integrate the professional behaviors and attitudes expected in their field. Furthermore, Sarraf-Yazdi et al. [ 12 ] highlighted that professional identity formation is a continuous and multifactorial process involving the interplay of individual values, beliefs, and environmental factors. This dynamic process is shaped by both clinical and non-clinical experiences within the learning environment [ 12 ].

Various learning theories, such as the Communities of Practice (CoP) theory [ 13 ], emphasize the link between learning environments and learning outcomes, including professional identity development. The CoP theory describes communities of professionals with a shared knowledge interest who learn through regular interaction [ 13 , 14 ]. Within the CoP, students transition from being peripheral observers to central members [ 15 ]. Therefore, the CoP theory suggests that a positive learning environment is crucial for fostering learning, professional identity formation, and a sense of community [ 16 ].

Undoubtedly, health professional education programs (e.g., Medicine, Dental Medicine, Pharmacy, and Health Sciences) play a vital role not only in shaping the knowledge, expertise, and abilities of health professional students but also in equipping them with the necessary competencies for implementing healthcare initiatives and strategies and responding to evolving healthcare demands [ 17 ]. Within the field of health professions education, international organizations like the United Nations Educational, Scientific, and Cultural Organization (UNESCO), European Union (EU), American Council on Education (ACE), and World Federation for Medical Education (WFME) have emphasized the importance of high-quality learning environments in fostering the development of future healthcare professionals and called for considerations of the enhancement of the quality of the learning environment of health profession education programs [ 18 , 19 ]. These environments are pivotal for nurturing both the academic and professional growth necessary to navigate an increasingly globalized healthcare landscape [ 18 , 19 ].

Professional identity development is integral to health professions education which evolves continuously from early university years until later stages of the professional life as a healthcare practitioner [ 20 , 21 ]. This ongoing development helps students establish clear professional roles and boundaries, thereby reducing role ambiguity within multidisciplinary teams [ 9 ]. It is expected that as students advance in their professional education, their perception of the quality of the learning environment changes, which influences their learning experiences, the development of their professional identity, and their sense of community [ 22 ]. Cruess et al. [ 23 ] asserted that medical schools foster professional identity through impactful learning experiences, effective role models, clear curricula, and assessments. A well-designed learning environment that incorporates these elements supports medical students' socialization and professional identity formation through structured learning, reflective practices, and constructive feedback in both preclinical and clinical stages [ 23 ].

Despite the recognized importance of the quality of learning environments and their influence on student-related outcomes, this topic has been overlooked regionally and globally [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. There is a significant knowledge gap in understanding how different components of the learning environment specifically contribute to professional identity formation. Most existing studies focus on general educational outcomes without exploring the detailed ways in which the learning environment shapes professional attitudes, values, and identity. Moreover, there is a global scarcity of research exploring how students’ perceptions of the quality of the learning environment and professional identity vary across various health profession education programs at different stages of their undergraduate education. This lack of comparative studies makes it challenging to identify best practices that can be adapted across different educational contexts. Furthermore, most research tends to focus on single-discipline studies, neglecting the interdisciplinary nature of modern healthcare education, which is essential for preparing students for collaborative practice in real-world healthcare settings. Considering the complex and demanding nature of health profession education programs and the increased emphasis on the quality of learning environments by accreditation bodies, examining the perceived quality of the educational learning environment by students is crucial [ 19 ]. Understanding students’ perspectives can provide valuable insights into areas needing improvement and highlight successful strategies that enhance both learning environment and experiences and professional identity development.

This research addresses this gap by focusing on the interdisciplinary health profession education programs to understand the impact of the learning environment on the development of the professional identity of students and its overall influence on their learning experiences. The objectives of this study are to 1) examine the perception of health professional students of the quality of their learning environment and their professional identity, 2) identify the association between health professional students’ perception of the quality of their learning environment and the development of their professional identity, and 3) explore the expectations of health professional students of the ideal educational learning environment. This research is essential in providing insights to inform educational practices globally to develop strategies to enhance the quality of health profession education.

Study setting and design

This study was conducted at Qatar University Health (QU Health) Cluster which is an interdisciplinary health profession education program that was introduced as the national provider of higher education in health and medicine in the state of Qatar. QU Health incorporates five colleges: Health Sciences (CHS), Pharmacy (CPH), Medicine (CMED), Dental Medicine (CDEM) and Nursing (CNUR) [ 31 ]. QU Health is dedicated to advancing inter-professional education (IPE) through its comprehensive interdisciplinary programs. By integrating IPE principles into the curriculum and fostering collaboration across various healthcare disciplines, the cluster prepares students to become skilled and collaborative professionals. Its holistic approach to teaching, research, and community engagement not only enhances the educational experience but also addresses local and regional healthcare challenges, thereby making a significant contribution to the advancement of population health in Qatar [ 32 ]. This study was conducted from November 2022 to July 2023. An explanatory sequential mixed methods triangulation approach was used for an in-depth exploration and validation of the quantitative results qualitatively [ 33 , 34 ]. Ethical approval for the study was obtained from the Qatar University Institutional Review Board (approval number: QU-IRB 1734-EA/22).

For the quantitative phase, a questionnaire was administered via SurveyMonkey® incorporating two previously validated questionnaires: the Dundee Ready Educational Environment Measure (DREEM), developed by Roff et al. in 1997 [ 35 ], and the Macleod Clark Professional Identity Scale-9 (MCPIS-9), developed by Adam et al. in 2006 [ 8 ]. Integrating DREEM and MCPIS-9 into a single questionnaire was undertaken to facilitate a comprehensive evaluation of two distinct yet complementary dimensions—namely, the educational environment and professional identity—that collectively influence the learning experience and outcomes of students, as no single instrument effectively assesses both aspects simultaneously [ 36 ]. The survey comprised three sections—Section A: sociodemographic characteristics, Section B: the DREEM scoring scale for assessing the quality of the learning environment, and Section C: the MCPIS-9 scoring scale for assessing professional identity. For the qualitative phase, seven focus groups (FGs) were arranged with a sample of QU-Health students. The qualitative and quantitative data obtained were integrated at the interpretation and reporting level using a narrative, contiguous approach [ 37 , 38 ].

Quantitative phase

Population and sampling.

The total population sampling approach in which all undergraduate QU-Health students who had declared their majors (i.e., the primary field of study that an undergraduate student has chosen during their academic program) at the time of conducting the study in any of the four health colleges under QU-Health ( N = 908), namely, CPH, CMED, CDEM, and CHS, such as Human Nutrition (Nut), Biomedical Science (Biomed), Public Health (PH), and Physiotherapy (PS), were invited to participate in the study. Nursing students were excluded from this study because the college was just established in 2022; therefore, students were in their general year and had yet to declare their majors at the time of the study. The minimum sample size required for the study was determined to be 271 students based on a margin error of 5%, a confidence level of 95%, and a response distribution of 50%.

Data collection

Data was collected in a cross-sectional design. After obtaining the approval of the head of each department, contact information for eligible students was extracted from the QU-Health student databases for each college, and invitations were sent via email. The distribution of these invitations was done by the administrators of the respective colleges. The invitation included a link to a self-administered questionnaire on SurveyMonkey® (Survey Monkey Inc., San Mateo, California, USA), along with informed consent information. All 908 students were informed about the study’s purpose, data collection process, anonymity and confidentiality assurance, and the voluntary nature of participation. The participants were sent regular reminders to complete the survey to increase the response rate.

A focused literature review identified the DREEM as the most suitable validated tool for this study. The DREEM is considered the gold standard for assessing undergraduate students' perceptions of their learning environment [ 35 ]. Its validity and reliability have been consistently demonstrated across various settings (i.e., clinical and non-clinical) and health professions (e.g., nursing, medicine, dentistry, and pharmacy), in multiple countries worldwide, including the Gulf Cooperation Council countries [ 24 , 35 , 39 , 40 , 41 , 42 ]. The DREEM is a 50-item inventory divided into 5 subscales and developed to measure the academic climate of educational institutions using a five-point Likert scale from 0 “strongly disagree” to 4 “strongly agree”. The total score ranges from 0 to 200, with higher scores reflecting better perceptions of the learning environment [ 35 , 39 , 43 ]. The interpretation includes very poor (0–50), plenty of problems (51–100), more positive than negative (101–151), and excellent (151–200).

The first subscale, Perception to Learning (SpoL), with 12 items scoring 0–48. Interpretation includes very poor (0–12), teaching is viewed negatively (13–24), a more positive approach (25–36), and teaching is highly thought of (37–48). The second domain, Perception to Teachers (SpoT), with 11 items scoring 0–44. Interpretation includes abysmal (0–11), in need of some retraining (12–22), moving in the right direction (23–33), and model teachers (34–44). The third domain, academic self-perception (SASP), with 8 items scoring 0–32. Interpretation includes a feeling of total failure (0–8), many negative aspects (9–16), feeling more on the positive side (17–24), and confident (25–32). The fourth domain, Perception of the atmosphere (SPoA), with 12 items scoring 0–48. Interpretation includes a terrible environment (0–12); many issues need to be changed (13–24), a more positive atmosphere (25–36), and a good feeling overall (37–48). Lastly, the fifth domain, social self-perception (SSSP), with 7 items scoring 0–28. Interpretation includes Miserable (0–7), Not a nice place (8–14), Not very bad (15–21), and very good socially (22–28).

Several tools have been developed to explore professional identity in health professions [ 44 ], but there is limited research on their psychometric qualities [ 45 ]. The MCPIS-9 is notable for its robust psychometric validation and was chosen for this study due to its effectiveness in a multidisciplinary context as opposed to other questionnaires that were initially developed for the nursing profession [ 8 , 46 , 47 ]. MCPIS-9 is a validated 9-item instrument, which uses a 5-point Likert response scale, with scores ranging from 1 “strongly disagree” to 5 “strongly agree”. Previous studies that utilized the MCPIS-9 had no universal guidance for interpreting the MCPIS-9 score; however, the higher the score, the stronger the sense of professional identity [ 46 , 48 ].

Data analysis

The quantitative data were analyzed using SPSS software (IBM SPSS Statistics for Windows, version 27.0; IBM Corp., Armonk, NY, USA). The original developers of the DREEM inventory identified nine negative items: items 11, 12, 19, 20, 21, 23, 42, 43, and 46 – these items were reverse-coded. Additionally, in the MCPIS-9 tool, the original developers identified three negative items: items 3, 4, and 5. Descriptive and inferential analyses were also conducted. Descriptive statistics including number (frequencies [%]), mean ± SD, and median (IQR), were used to summarize the demographics and responses to the DREEM and MCPIS-9 scoring scales. In the inferential analysis, to test for significant differences between demographic subgroups in the DREEM and MCPIS-9 scores, Kruskal–Wallis tests were used for variables with more than two categories, and Mann–Whitney U-tests were used for variables with two categories. Spearman's rank correlation analysis was used to investigate the association between perceived learning environment and professional identity development. The level of statistical significance was set a priori at p < 0.05. The internal consistency of the DREEM and MCPIS-9 tools was tested against the acceptable Cronbach's alpha value of 0.7.

Qualitative phase

A purposive sampling approach was employed to select students who were most likely to provide valuable insights to gain a deeper understanding of the topic. The inclusion criteria required that participants should have declared their major in one of the following programs: CPH, CMED, CDEM, CHS: Nut, Biomed, PS, and PH. This selection criterion aimed to ensure that participants had sufficient knowledge and experience related to their chosen fields of study within QU-Health. Students were included if they were available and willing to share their experiences and thoughts. Students who did not meet these criteria were excluded from participation. To ensure a representative sample, seven FGs were conducted, one with each health professional education program. After obtaining the approval of the head of each department, participants were recruited by contacting the class representative of each professional year to ask for volunteers to join and provide their insights. Each FG involved students from different professional years to ensure a diverse representation of experiences and perspectives.

The topic guide (Supplementary Material 1) was developed and conceptualized based on the research objectives, selected results from the quantitative phase, and the Gruppen et. al. framework [ 1 ]. FGs were conducted online using Microsoft Teams® through synchronous meetings. Before initiating the FGs, participants were informed of their rights and returned signed consent forms to the researchers. FGs were facilitated by two research assistants (AA and OY), each facilitating separate sessions. The facilitators, who had prior experience with conducting FGs and who were former pharmacy students from the CPH, were familiar with some of the participants, and hence were able to encourage open discussion, making it easier for students to share their perceptions of the learning environment within the QU Health Cluster. Participants engaged in concurrent discussions were encouraged to use the "raise hand" feature on Microsoft Teams to mimic face-to-face interactions. Each FG lasted 45–60 min, was conducted in English, and was recorded and transcribed verbatim and double-checked for accuracy. After the seventh FG, the researchers were confident that a saturation point had been reached where no new ideas emerged, and any further data collection through FGs was unnecessary. Peer and supervisory audits were conducted throughout the research process.

The NVIVO ® software (version 12) was utilized to perform a thematic analysis incorporating both deductive and inductive approaches. The deductive approach involved organizing the data into pre-determined categories based on the Gruppen et al. framework, which outlines key components of the learning environment. This framework enabled a systematic analysis of how each component of the learning environment contributes to students' professional development and highlighted areas for potential improvement. Concurrently, the inductive approach was applied to explore students' perceptions of an ideal learning environment, facilitating the emergence of new themes and insights directly from the data, independent of pre-existing categories. This dual approach provided a comprehensive understanding of the data by validating the existing theory while also exploring new findings [ 49 ]. Two coders were involved in coding the transcripts (AA and BM) and in cases of disagreements between researchers, consensus was achieved through discussion.

The response rate was 57.8% (525 responses out of 908), while the usability rate was 74.3% (390 responses out of 525) after excluding students who only completed the demographic section. The demographic and professional characteristics of the participants are presented in Table 1 . The majority were Qataris (37.0% [ n = 142]), females (85.1% [ n = 332]), and of the age group of 21–23 years (51.7% [ n = 201]). The students were predominantly studying at the CHS (36.9%[ n = 144]), in their second professional year (37.4% [ n = 146]), and had yet to be exposed to experiential learning, that is, clinical rotations (70.2% [ n = 273]).

Perceptions of students of their learning environment

The overall median DREEM score for study participants indicated that QU Health students perceive their learning environment to be "more positive than negative" (132 [IQR = 116–174]). The reliability analysis for this sample of participants indicated a Cronbach's alpha for the total DREEM score of 0.94, and Cronbach's alpha scores for each domain of the DREEM tool, SPoL, SPoT, SASP, SPoA, and SSSP of 0.85, 0.74, 0.81, 0.85, and 0.65, respectively.

Individual item responses representing each domain of the DREEM tool are presented in Table 2 . For Domain I, QU Health students perceived the teaching approach in QU Health to be "more positive" (32 [IQR = 27–36]). Numerous participants agreed that the teaching was well-focused (70.7% [ n = 274]), student-focused (66.1% [ n = 254]) and aimed to develop the competencies of students (72.0% [ n = 278]). The analysis of students’ perceptions related to Domain II revealed that faculty members were perceived to be “moving in the right direction” (30 [IQR = 26–34]). Most students agreed that faculty members were knowledgeable (90.7%[ n = 345]) and provided students with clear examples and constructive feedback (77.6% [ n = 294] and 63.8% [ n = 224], respectively. Furthermore, the analysis of Domain III demonstrated that QU Health students were shown to have a "positive academic self-perception" (22 [IQR = 19–25]). In this regard, most students believed that they were developing their problem-solving skills (78% [ n = 292]) and that what they learned was relevant to their professional careers (76% [ n = 288]). Furthermore, approximately 80% ( n = 306) of students agreed that they had learned empathy in their profession. For Domain IV, students perceived the atmosphere of their learning environment to be "more positive" (32 [IQR = 14–19]). A substantial number of students asserted that there were opportunities for them to develop interpersonal skills (77.7% [ n = 293]), and that the atmosphere motivated them as learners (63.0% [ n = 235]). Approximately one-third of students believed that the enjoyment did not outweigh the stress of studying (32.3% [ n = 174]). Finally, analysis of Domain V indicates that students’ social self-perception was “not very bad” (17 [IQR = 27–36]). Most students agreed that they had good friends at their colleges (83% [ n = 314]) and that their social lives were good (68% [ n = 254]).

Table 3 illustrates the differences in the perception of students of their overall learning environment according to their demographic and professional characteristics. No significant differences were noted in the perception of the learning environment among the subgroups with selected demographic and professional characteristics, except for the health profession program in which they were enrolled ( p -value < 0.001), whether they had relatives who studied or had studied the same profession ( p -value < 0.002), and whether they started their experiential learning ( p -value = 0.043). Further analyses comparing the DREEM subscale scores according to their demographic and professional characteristics are presented in Supplementary Material 1.

Students’ perceptions of their professional identities

The students provided positive responses relating to their perceptions of their professional identity (24.00 IQR = [22–27]). The reliability analysis of this sample indicated a Cronbach's alpha of 0.605. The individual item responses representing the MCPIS-9 tool are presented in Table 2 . Most students (85% [ n = 297]) expressed pleasant feelings about belonging to their own profession, and 81% ( n = 280) identified positively with members of their profession. No significant differences were noted in the perception of students of their professional identity when analyzed against selected demographic subgroups, except for whether they had relatives who had studied or were studying the same profession ( p -value = 0.027). Students who had relatives studying or had studied the same profession tended to perceive their professional identity better (25 IQR = [22–27] and 24 IQR = [21–26], respectively) (Table 3 ).

Association between MCPIS-9 and DREEM

Spearman's rank correlation between the DREEM and MCPIS-9 total scores indicated an intermediate positive correlation between perceptions of students toward their learning environment and their professional identity development (r = 0.442, p -value < 0.001). The DREEM questionnaire, with its 50 items divided into five subscales, comprehensively assessed various dimensions of the learning environment. Each subscale evaluated a distinct aspect of the educational experience, such as the effectiveness of teaching, teacher behavior and attitudes, academic confidence, the overall learning atmosphere, and social integration. The MCPIS-9 questionnaire specifically assessed professional identity through nine items that measure attitudes, values, and self-perceived competence in the professional domain. The positive correlation demonstrated between the DREEM and MCPIS-9 scores indicated that as students perceive their learning environment more positively, their professional identity is also enhanced.

Thirty-seven students from the QU Health colleges were interviewed: eleven from CPH, eight from CMED, four from CDEM, and fourteen from CHS (six from Nut, three from PS, three from Biomed, and three from PH). Four conventional themes were generated deductively using Gruppen et al.’s conceptual framework, while one theme was derived through inductive analysis. The themes and sub-themes generated are demonstrated in Table 4 .

Theme 1. The personal component of the learning environment

This theme focused on student interactions and experiences within their learning environment and their impact on perceptions of learning, processes, growth, and professional development.

Sub-theme 1.1. Experiences influencing professional identity formation

Students classified their experiences into positive and negative. Positive experiences included hands-on activities such as on-campus practical courses and pre-clinical activities, which built their confidence and professional identity. In this regard, one student mentioned:

“Practical courses are one of the most important courses to help us develop into pharmacists. They make you feel confident in your knowledge and more willing to share what you know.” [CPH-5]

Many students claimed that interprofessional education (IPE) activities enhanced their self-perception, clarified their roles, and boosted their professional identity and confidence. An interviewee stated:

"I believe that the IPE activity,…., is an opportunity for us to explore our role. It has made me know where my profession stands in the health sector and how we all depend on each other through interprofessional thinking and discussions." [CHS-Nut-32]

However, several participants reported that an extensive workload hindered their professional identity development. A participant stated:

“The excessive workload prevents us from joining activities that would contribute to our professional identity development. Also, it restricts our networking opportunities and makes us always feel burnt out.” [CHS-Nut-31]

Sub-theme 1.2. Strategies used by students to pursue their goals

QU Health students employed various academic and non-academic strategies to achieve their objectives, with many emphasizing list-making and identifying effective study methods as key approaches:

“Documentation. I like to see tasks that I need to do on paper. Also, I like to classify my tasks based on their urgency. I mean, deadlines.” [CHS-Nut-31]

“I always try to be as efficient as possible when studying and this can be by knowing what studying method best suits me.” [CHS-Biomed-35]

Nearly all students agreed that seeking feedback from faculty was crucial for improving their work and performance. In this context, a student said:

“We must take advantage of the provided opportunity to discuss our assignments, projects, and exams, like what we did correctly, and what we did wrongly. They always discuss with us how to improve our work on these things.” [CHS-Nut-32]

Moreover, many students also believed that developing communication skills was vital for achieving their goals, given their future roles in interprofessional teams. A student mentioned:

“Improving your communication skills is a must because inshallah (with God’s will) in the future we will not only work with biomedical scientists, but also with nurses, pharmacists, and doctors. So, you must have good communication abilities.” [CHS-Biomed-34]

Finally, students believe that networking is crucial for achieving their goals because it opens new opportunities for them as stated by a student:

“Networking with different physicians or professors can help you to know about research or training opportunities that you could potentially join.” [CMED-15]

Subtheme 1.3. Students’ mental and physical well-being

Students agreed that while emotional well-being is crucial for good learning experiences and professional identity development, colleges offered insufficient support. An interviewee stated:

“We simply don't have the optimal support we need to take care of our emotional well-being as of now, despite how important it is and how it truly reflects on our learning and professional development” [CDEM-20]

Another student added:

“…being in an optimal mental state provides us with the opportunity to acquire all required skills that would aid in our professional identity development. I mean, interpersonal skills, adaptability, self-reflection” [CPH-9]

Students mentioned some emotional support provided by colleges, such as progress tracking and stress-relief activities. Students said:

“During P2 [professional year 2], I missed a quiz, and I was late for several lectures. Our learning support specialist contacted me … She was like, are you doing fine? I explained everything to her, and she contacted the professors for their consideration and support.” [CPH-7]

“There are important events that are done to make students take a break and recharge, but they are not consistent” [CHS-PS-27]

On the physical well-being front, students felt that their colleges ensured safety, especially in lab settings, with proper protocols to avoid harm. A student mentioned:

“The professors and staff duly ensure our safety, especially during lab work. They make sure that we don't go near any harmful substances and that we abide by the lab safety rules” [CHS-Biomed -35]

Theme 2. Social component of the learning environment

This theme focused on how social interactions shape students’ perceptions of learning environments and learning experiences.

Sub-theme 2.1. Opportunities for community engagement

Participants identified various opportunities for social interactions through curricular and extracurricular activities. Project-based learning (PBL) helped them build connections, improve teamwork and enhance critical thinking and responsibility as stated by one student:

“I believe that having PBL as a big part of our learning process improves our teamwork and interpersonal skills and makes us take responsibility in learning, thinking critically, and going beyond what we would have received in class to prepare very well and deep into the topic.” [CMED-12]

Extracurricular activities, including campaigns and events, helped students expand their social relationships and manage emotional stress. A student stated:

“I think that the extracurricular activities that we do, like the campaigns or other things that we hold in the college with other students from other colleges, have been helpful for me in developing my personality and widening my social circle. Also, it dilutes the emotional stress we are experiencing in class” [CDEM-22]

Sub-theme 2.2. Opportunities for learner-to-patient interactions

Students noted several approaches their colleges used to enhance patient-centered education and prepare them for real-world patient interactions. These approaches include communication skills classes, simulated patient scenarios, and field trips. Students mentioned:

“We took a class called Foundation of Health, which mainly focused on how to communicate our message to patients to ensure that they were getting optimal care. This course made us appreciate the term ‘patient care’ more.” [CHS-PH-38]

“We began to appreciate patient care when we started to take a professional skills course that entailed the implementation of a simulated patient scenario. We started to realize that communication with patients didn’t go as smoothly as when we did it with a colleague in the classroom.” [CPH-1]

“We went on a field trip to ‘Shafallah Center for Persons with Disability’ and that helped us to realize that there were a variety of patients that we had to care for, and we should be physically and mentally prepared to meet their needs.” [CDEM-21]

Theme 3. Organizational component of the learning environment

This theme explored students' perceptions of how the college administration, policies, culture, coordination, and curriculum design impact their learning experiences.

Sub-theme 3.1. Curriculum and study plan

Students valued clinical placements for their role in preparing them for the workplace and developing professional identity. A student stated:

“Clinical placements are very crucial for our professional identity development; we get the opportunity to be familiarized with and prepared for the work environment.” [CHS-PS-27]

However, students criticized their curriculum for not equipping them with adequate knowledge and skills. For example, a student said:

“… Not having a well-designed curriculum is of concern. We started very late in studying dentistry stuff and that led to us cramming all the necessary information that we should have learned.” [CDEM-20]

Furthermore, students reported that demanding schedules and limited course availability hindered learning and delayed progress:

“Last semester, I had classes from Sunday to Thursday from 8:00 AM till 3:00 PM in the same classroom, back-to-back, without any break. I was unable to focus in the second half of the day.” [CHS-Nut-38]

“Some courses are only offered once a year, and they are sometimes prerequisites for other courses. This can delay our clinical internship or graduation by one year.” [CHS-Biomed-36]

Additionally, the outdated curriculum was seen as misaligned with advancements in artificial intelligence (AI). One student stated:

“… What we learn in our labs is old-fashioned techniques, while Hamad Medical Corporation (HMC) is following a new protocol that uses automation and AI. So, I believe that we need to get on track with HMC as most of us will be working there after graduation.” [CHS-Biomed-35]

Sub-theme 3.2. Organizational climate and policies

Students generally appreciated the positive university climate and effective communication with the college administration which improves course quality:

“Faculty members and the college administration usually listen to our comments about courses or anything that we want to improve, and by providing a course evaluation at the end of the semester, things get better eventually.” [CPH-2]

Students also valued faculty flexibility with scheduling exams and assignments, and praised the new makeup exam policy which enhances focus on learning:

“Faculty members are very lenient with us. If we want to change the date of the exam or the deadline for any assignment, they agree if everyone in the class agrees. They prioritize the quality of our work over just getting an assignment done.” [CHS-PS-37]

“I am happy with the introduction of makeup exams. Now, we are not afraid of failing and losing a whole year because of a course. I believe that this will help us to focus on topics, not just cramming the knowledge to pass.” [CPH-9]

However, students expressed concerns about the lack of communication between colleges and clinical placements and criticized the lengthy approval process for extracurricular activities:

“There is a contract between QU and HMC, but the lack of communication between them puts students in a grey area. I wish there would be better communication between them.” [CMED-15]

“To get a club approved by QU, you must go through various barriers, and it doesn't work every time. A lot of times you won't get approved.” [CMED-14]

Theme 4. Materialistic component of the learning environment

This theme discussed how physical and virtual learning spaces affect students' learning experiences and professional identity.

Sub-theme 4.1. The physical space for learning

Students explained that the interior design of buildings and the fully equipped laboratory facilities in their programs enhanced focus and learning:

“The design has a calming effect, all walls are simple and isolate the noise, the classrooms are big with big windows, so that the sunlight enters easily, and we can see the green grass. This is very important for focusing and optimal learning outcomes.” [CPH-5]

“In our labs, we have beds and all the required machines for physiotherapy exercises and practical training, and we can practice with each other freely.” [CHS-PS-27]

Students from different emphasized the need for dedicated lecture rooms for each batch and highlighted the importance of having on-site cafeterias to avoid disruptions during the day:

“We don't have lecture rooms devoted to each batch. Sometimes we don't even find a room to attend lectures and we end up taking the lectures in the lab, which makes it hard for us to focus and study later.” [CDEM-23]

“Not having a cafeteria in this building is a negative point. Sometimes we miss the next lecture or part of it if we go to another building to buy breakfast.” [CHS-Nut-29]

Sub-theme 4.2. The virtual space for online learning

Students appreciated the university library's extensive online resources and free access to platforms like Microsoft Teams and Webex for efficient learning and meetings. They valued recorded lectures for flexible study and appreciated virtual webinars and workshops for global connectivity.

“QU Library provides us with a great diversity and a good number of resources, like journals or books, as well as access medicine, massive open online courses, and other platforms that are very useful for studying.” [CMED-16].

“Having your lectures recorded through virtual platforms made it easier to take notes efficiently and to study at my own pace.” [CHS-PS-38]

"I hold a genuine appreciation for the provided opportunities to register in online conferences. I remember during the COVID-19 pandemic, I got the chance to attend an online workshop. This experience allowed me to connect with so many people from around the world." [CMED-15]

Theme 5. Characteristics of an ideal learning environment

This theme explored students’ perceptions of an ideal learning environment and its impact on their professional development and identity.

Sub-theme 5.1. Active learning and professional development supporting environment

Students highlighted that an ideal learning environment should incorporate active learning methods and a supportive atmosphere. They suggested using simulated patients in case-based learning and the use of game-based learning platforms:

“I think if we have, like in ITQAN [a Clinical Simulation and Innovation Center located on the Hamad Bin Khalifa Medical City (HBKMC) campus of Hamad Medical Corporation (HMC)], simulated patients, I think that will be perfect like in an “Integrated Case-Based Learning” case or professional skills or patient assessment labs where we can go and intervene with simulated patients and see what happens as a consequence. This will facilitate our learning.” [CPH-4]

“I feel that ‘Kahoot’ activities add a lot to the session. We get motivated and excited to solve questions and win. We keep laughing, and I honestly feel that the answers to these questions get stuck in my head.” [CHS-PH-38].

Students emphasized the need for more opportunities for research, career planning, and equity in terms of providing resources and opportunities for students:

“Students should be provided with more opportunities to do research, publish, and practice.” [CMED-16]

“We need better career planning and workshops or advice regarding what we do after graduation or what opportunities we have.” [CHS-PS-25]

“I think that opportunities are disproportionate, and this is not ideal. I believe all students should have the same access to opportunities like having the chance to participate in conferences and receiving research opportunities, especially if one fulfills the requirements.” [CHS-Biomed-35]

Furthermore, the students proposed the implementation of mentorship programs and a reward system to enable a better learning experience:

“Something that could enable our personal development is a mentorship program, which our college started to implement this year, and I hope they continue to because it’s an attribute of an ideal learning environment.” [CPH-11]

“There has to be some form of reward or acknowledgments to students, especially those who, for example, have papers published or belong to leading clubs, not just those who are, for example, on a dean’s list because education is much more than just academics.” [CHS-PS-26]

Subtheme 5.2. Supportive physical environment

Participants emphasized that the physical environment of the college significantly influences their learning attitudes. A student said:

“The first thing that we encounter when we arrive at the university is the campus. I mean, our early thoughts toward our learning environment are formed before we even know anything about our faculty members or the provided facilities. So, ideally, it starts here.” [CPH-10]

Therefore, students identified key characteristics of an optimal physical environment which included: having a walkable campus, designated study and social areas, and accessible food and coffee.

“I think that learning in what they refer to as a walkable campus, which entails having the colleges and facilities within walking distance from each other, without restrictions of high temperature and slow transportation, is ideal.” [CPH-8]

“The classrooms and library should be conducive to studying and focusing, and there should also be other places where one can actually socialize and sit with one’s friends.” [CDEM-22]

“It is really important to have a food court or café in each building, as our schedules are already packed, and we have no time to go get anything for nearby buildings.” [CHS-Biomed-34]

Data integration

Table 5 represents the integration of data from the quantitative and qualitative phases. It demonstrates how the quantitative findings informed and complemented the qualitative analysis and explains how quantitative data guided the selection of themes in the qualitative phase. The integration of quantitative and qualitative data revealed both convergences and divergences in students' views of their learning environment. Both data sources consistently indicated that the learning environment supported the development of interpersonal skills, fostered strong relationships with faculty, and promoted an active, student-centered learning approach. This environment was credited with enhancing critical thinking, independence, and responsibility, as well as boosting students' confidence and competence through clear role definitions and constructive faculty feedback.

However, discrepancies emerged between the two phases. Quantitative data suggested general satisfaction with timetables and support systems, while qualitative data uncovered significant dissatisfaction. Although quantitative results indicated that students felt well-prepared and able to memorize necessary material, qualitative findings revealed challenges with concentration and focus. Furthermore, while quantitative data showed contentment with institutional support, qualitative responses pointed to shortcomings in emotional and physical support.

This study examined the perceptions of QU Health students regarding the quality of their learning environment and the characteristics of an ideal learning environment. Moreover, this study offered insights into the development of professional identity, emphasizing the multifaceted nature of learning environments and their substantial impact on professional identity formation.

Perceptions of the learning environment

The findings revealed predominantly positive perceptions among students regarding the quality of the overall learning environment at QU Health and generally favorable perception of all five DREEM subscales, which is consistent with the international studies using the DREEM tool [ 43 , 50 , 51 , 52 , 53 , 54 ]. Specifically, participants engaged in experiential learning expressed heightened satisfaction, which aligns with existing research indicating that practical educational approaches enhance student engagement and satisfaction [ 55 , 56 ]. Additionally, despite limited literature, students without relatives in the same profession demonstrated higher perceptions of their learning environment, possibly due to fewer preconceived expectations. A 2023 systematic review highlighted how students’ expectations influence their satisfaction and academic achievement [ 57 ]. However, specific concerns arose regarding the learning environment, including overemphasis on factual learning in teaching, student fatigue, and occasional boredom. These issues were closely linked to the overwhelming workload and conventional teaching methods, as identified in the qualitative phase.

Association between learning environment and professional identity

This study uniquely integrated the perceptions of the learning environment with insights into professional identity formation in the context of healthcare education which is a relatively underexplored area in quantitative studies [ 44 , 58 , 59 , 60 ]. This study demonstrated a positive correlation between students' perceptions of the learning environment (DREEM) and their professional identity development (MCPIS-9) which suggested that a more positive learning environment is associated with enhanced professional identity formation. For example, a supportive and comfortable learning atmosphere (i.e., high SPoA scores) can enhance students' confidence and professional self-perception (i.e., high MCPIS-9 scores). The relationship between these questionnaires is fundamental to this study. The DREEM subscales, particularly Perception of Learning (SpoL) and Academic Self-Perception (SASP), relate to how the learning environment supports or hinders the development of a professional identity, as measured by MCPIS-9. Furthermore, the Perception of Teachers (SpoT) subscale examines how teacher behaviors and attitudes impact students, which can influence their professional identity development. The Perception of Atmosphere (SPoA) and Social Self-Perception (SSSP) subscales evaluate the broader environment and social interactions, which are crucial for professional identity formation as they foster a sense of community and belonging.

Employing a mixed methods approach and analyzing both questionnaires and FGs through the framework outlined by Gruppen et al. highlighted key aspects across four dimensions of the learning environment: personal development, social dimension, organizational setting, and materialistic dimension [ 1 ]. First, the study underscored the significance of both personal development and constructive feedback. IPE activities emerged as a key factor that promotes professional identity by cultivating collaboration and role identification which is consistent with Bendowska and Baum's findings [ 61 ]. Similarly, the positive impact of constructive faculty feedback on student learning outcomes aligned with the work of Gan et al. which revealed that feedback from faculty members positively influences course satisfaction and knowledge retention, which are usually reflected in course results [ 62 ]. Importantly, the research also emphasized the need for workload management strategies to mitigate negative impacts on student well-being, a crucial factor for academic performance and professional identity development [ 63 , 64 ]. The inclusion of community events and support services could play a significant role in fostering student well-being and reducing stress, as suggested by Hoferichter et al. [ 65 ]. Second, the importance of the social dimension of the learning environment was further highlighted by the study. Extracurricular activities were identified as opportunities to develop essential interpersonal skills needed for professional identity, mirroring the conclusions drawn by Achar Fujii et al. who argued that extracurricular activities lead to the development of fundamental skills and attitudes to build and refine their professional identity and facilitate the learning process, such as leadership, commitment, and responsibility [ 66 ]. Furthermore, Magpantay-Monroe et al. concluded that community and social engagement led to professional identity development in nursing students through the expansion of their knowledge and communication with other nursing professionals [ 67 ]. PBL activities were another key element that promoted critical thinking, learning, and ultimately, professional identity development in this study similar to what was reported by Zhou et al. and Du et al. [ 68 , 69 ]. Third, the organizational setting, particularly the curriculum and clinical experiences, emerged as crucial factors. Clinical placements and field trips were found to be instrumental in cultivating empathy and professional identity [ 70 , 71 ]. However, maintaining an up-to-date curriculum that reflects advancements in AI healthcare education is equally important, as highlighted by Randhawa and Jackson in 2019 [ 72 ]. Finally, the study underlined the role of the materialistic dimension of the learning environment. Physical learning environments with natural light and managed noise levels were found to contribute to improved academic performance [ 73 , 74 ]. Additionally, the value of online educational resources, such as online library resources and massive open online course, as tools facilitating learning by providing easy access to materials, was emphasized, which is consistent with the observations of Haleem et al. [ 75 ].

The above collectively contribute to shaping students' professional identities through appreciating their roles, developing confidence, and understanding the interdependence of different health professions. These indicate that a supportive and engaging learning environment is crucial for fostering a strong sense of professional identity. Incorporating these student-informed strategies can assist educational institutions in cultivating well-rounded healthcare professionals equipped with the knowledge, skills, and emotional resilience needed to thrive in the dynamic healthcare landscape. Compared to existing quantitative data, this study reported a lower median MCPIS-9 score of 24.0, in contrast to previously reported scores of 39.0, 38.0, 38.0, respectively. [ 76 , 77 , 78 ]. This discrepancy may be influenced by the fact that the participants were in their second professional year, known for weaker identity development [ 79 ]. Students with relatives in the same profession perceived their identity more positively, which is likely due to role model influences [ 22 ].

Expectations of the ideal educational learning environment

This study also sought to identify the key attributes of an ideal learning environment from the perspective of students at QU-Health. The findings revealed a strong emphasis on active learning strategies, aligning with Kolb's experiential learning theory [ 80 ]. This preference suggests a desire to move beyond traditional lecture formats and engage in activities that promote experimentation and reflection, potentially mitigating issues of student boredom. Furthermore, students valued the implementation of simple reward systems such as public recognition, mirroring the positive impact such practices have on academic achievement reported by Dannan in 2020 [ 81 ]. The perceived importance of mentorship programs resonates with the work of Guhan et al. who demonstrated improved academic performance, particularly for struggling students [ 82 ]. Finally, the study highlighted the significance of a walkable campus with accessible facilities. This aligns with Rohana et al. who argued that readily available and useable facilities contribute to effective teaching and learning processes, ultimately resulting in improved student outcomes [ 83 ]. Understanding these student perceptions, health professions education programs can inform strategic planning for curricular and extracurricular modifications alongside infrastructural development.

The complementary nature of qualitative and quantitative methods in understanding student experiences

This study underscored the benefits of employing mixed methods to comprehensively explore the interplay between the learning environment and professional identity formation as complex phenomena. The qualitative component provided nuanced insights that complemented the baseline data provided by DREEM and MCPIS-9 questionnaires. While DREEM scores generally indicated positive perceptions, qualitative findings highlighted the significant impact of experiential learning on students' perceptions of the learning environment and professional identity development. Conversely, discrepancies emerged between questionnaire responses and FG interviews, revealing deeper issues such as fatigue and boredom associated with traditional teaching methods and heavy workloads, potentially influenced by cultural factors. In FGs, students revealed cultural pressures to conform and stigma against expressing dissatisfaction, which questionnaire responses may not capture. Qualitative data allowed students to openly discuss culturally sensitive issues, indicating that interviews complement surveys by revealing insights overlooked in quantitative assessments alone. These insights can inform the design of learning environments that support holistic student development. The study also suggested that cultural factors can influence student perceptions and should be considered in educational research and practice.

Application of findings

The findings from this study can be directly applied to inform and enhance educational practices, as well as to influence policy and practice sectors. Educational institutions should prioritize integrating active learning strategies and mentorship programs to combat issues such as student fatigue and boredom. Furthermore, practical opportunities, including experiential learning and IPE activities, should be emphasized to strengthen professional identity and engagement. To address these challenges comprehensively, policymakers should consider developing policies that support effective workload management and community support services, which are essential for improving student well-being and academic performance. Collaboration between educational institutions and practice sectors can greatly improve students' satisfaction with their learning environment and experience. This partnership enhances the relevance and engagement of their education, leading to a stronger professional identity and better preparation for successful careers.

Limitations

As with all research, this study has several limitations. For instance, there was a higher percentage of female participants compared to males; however, it is noteworthy to highlight the demographic composition of QU Health population, where students are majority female. Furthermore, the CHS, which is one of the participating colleges in this study, enrolls only female students. Another limitation is the potentially underpowered statistical comparisons among the sociodemographic characteristics in relation to the total DREEM and MCPIS-9 scores. Thus, the findings of this study should be interpreted with caution.

The findings of this study reveal that QU Health students generally hold a positive view of their learning environment and professional identity, with a significant positive correlation exists between students’ perceptions of their learning environment and their professional identity. Specifically, students who engaged in experiential learning or enrolled in practical programs rated their learning environment more favorably, and those with relatives in the same profession had a more positive view of their professional identity. The participants of this study also identified several key attributes that contribute to a positive learning environment, including active learning approaches and mentorship programs. Furthermore, addressing issues like fatigue and boredom is crucial for enhancing student satisfaction and professional development.

To build on these findings, future research should focus on longitudinal studies that monitor changes in the perceptions of students over time and identify the long-term impact of implementing the proposed attributes of an ideal learning environment on the learning process and professional identity development of students. Additionally, exploring the intricate dynamics of learning environments and their impact on professional identity can allow educators to better support students in their professional journey. Future research should also continue to explore these relationships, particularly on diverse cultural settings, in order to develop more inclusive and effective educational strategies. This approach will ensure that health professional students are well-prepared to meet the demands of their profession and provide high-quality care to their patients.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

United Nations Educational, Scientific, and Cultural Organization

European Union

American Council on Education

World Federation for Medical Education

Communities of Practice

Qatar University Health

College of Health Sciences

College of Pharmacy

College of Medicine

Dental Medicine

College of Nursing

Human Nutrition

Biomedical Science

Public Health

Physiotherapy

Dundee Ready Education Environment Measure

Perception to Learning

Perception to Teachers

Academic Self-Perception

Perception of the Atmosphere

Social Self-Perception

Macleod Clark Professional Identity Scale

Focus Group

InterProfessional Education

Project-Based Learning

Hamad Medical Corporation

Hamad Bin Khalifa Medical City

Artificial Intelligence

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Acknowledgements

The authors would like to thank all students who participated in this study.

This work was supported by the Qatar University Internal Collaborative Grant: QUCG-CPH-22/23–565.

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Mukhalalati, B., Aly, A., Yakti, O. et al. Examining the perception of undergraduate health professional students of their learning environment, learning experience and professional identity development: a mixed-methods study. BMC Med Educ 24 , 886 (2024). https://doi.org/10.1186/s12909-024-05875-4

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Life As A Graduate Student: Stressors, Mental Health Tips, And More

Whether you’ve finished college and have your bachelor’s degree or are still in school and looking ahead, the idea of going to graduate school may have crossed your mind. Pursuing a graduate degree may help you expand your skills and qualify for certain professions, but you might be curious about the challenges you could face as a grad student. Below, explore the realities of grad school, common mental health concerns among graduate students, and tips for taking care of yourself if you decide to pursue an advanced degree.

What does graduate education look like?

Some graduates start working after college, but others may want to take their studies further. Graduate studies let students continue their education after finishing their bachelor’s degree. Some professions, like law and medicine, require a graduate degree. You might also choose to go to graduate school to gain more knowledge in your chosen field or enhance your resume. Types of graduate degrees include the following.

Master’s degrees

Master’s programs provide a deeper focus on a specific area of study, such as fine arts or business. Some colleges offer special programs that let students get both a bachelor’s degree and a master’s degree in five years. However, individual master’s degrees typically take around two years to complete.

Doctorate degrees

Doctorate degrees are sometimes called “doctoral degrees.” These degrees are more advanced than master’s degrees and often take several years to complete. Doctorate students are often expected to conduct research in their chosen field and publish their findings.

Other graduate programs

Other graduate degrees include professional degrees and postgraduate certificates. Professional degrees provide training for specific careers, such as law or medicine. In contrast, postgraduate certificates are shorter programs that let graduates deepen their studies without pursuing a full degree.

Is there a difference between being a graduate student and being a postgraduate student?

If you’re still exploring graduate school, you may have encountered a few different terms describing similar programs. Two of the most common are “graduate” and “postgraduate.” While these terms might sound like different types of degrees, the terms “graduate studies” and “postgraduate studies” can be used interchangeably.

How is university different when pursuing a master’s or doctorate degree?

Some undergraduate students may wonder whether they can expect a similar college experience during grad school. Understanding what life is like as a graduate student may be useful if you’re considering taking the next step in your academic career. Below are a few potential differences between life as an undergraduate student and life as a graduate student.

Graduate university programs are often smaller

Graduate programs typically have fewer students than undergraduate programs. As a result, you may attend smaller, more intimate classes rather than large lectures. Graduate classes often focus more on group discussions , allowing students to build more of a relationship with their professors (and each other).

Graduate education tends to be more specific

Undergraduate students often take general education classes and electives to get their bachelor’s degrees. In graduate school, classes tend to be specific to your major. As a result, you may be able to explore your area of study in more depth than you would as an undergraduate.

You may do more research as a postgraduate student

Graduate degrees often focus more on independent study and research. As an undergraduate, you might take four to six classes per semester, while you might take half that many in graduate school. Outside class, you may be expected to complete research projects and manage your own coursework. Some graduate degree programs also require you to complete a thesis project or dissertation throughout your studies.

Unique challenges you may face in graduate school

Pursuing graduate studies can be a chance to gain valuable credentials and access to a broader range of career options. However, graduate school can also bring challenges to which you might not have been exposed as an undergraduate. Examples include the following.

The financial strain of being a new graduate student

As of 2023, a two-year graduate degree can cost $100,000 or more . Some graduate students may offset these costs through research grants or part-time work, but others may take out loans to pay for their tuition. Student debt can create a sense of pressure to finish your degree and find a well-paying job, contributing to stress throughout grad school. Carrying additional debt from your bachelor’s degree may intensify this stress more.

The stress of teaching classes

A growing number of graduate students are also employed by their schools , where they might assist with research or teach classes. Working as a student instructor can be a way to offset the costs of tuition and gain work experience. However, teaching can pose its own set of challenges, as not all student instructors receive preparation beforehand . Some may be expected to create lesson plans and grade assignments independently, which can create additional stress.

Juggling research, classwork, family, health, and wellness

Even if you don’t have a part-time job, you may still be busy during graduate school. Grad students are often expected to do their classwork and research on their own schedule. Balancing these academic demands with hobbies, relationships, and self-care can be challenging. If you’re one of the millions of grad students who is also a parent, you may face an even more demanding schedule.

Exploring the effects of grad school on mental health and wellness

Understanding how the challenges of graduate school can affect mental health may help you decide whether to continue your studies—and how to best maintain your mental health if you do. In 2022, the American College Health Association found that 77% of graduate and professional students experienced higher-than-average stress levels. Chronic stress has been linked to mental illnesses like depression and anxiety , as well as adverse health effects like headaches, heart disease, sleep issues, and weight changes.

While no two students are the same, and everyone reacts differently to stress, studies support that graduate students may face higher rates of mental illness. In 2018, researchers found that graduate students were over six times more likely than the general population to experience depression and anxiety. Two years later, the COVID-19 pandemic saw rates of graduate student mental illness increase by over 10% .

Tips for taking care of yourself as a new graduate student

Reading about the mental health effects of graduate school can be intimidating, especially if you’re still deciding whether to begin. However, know that mental illness and stress may not be inevitable. The following tips may help you limit these effects and find success as a grad student:

Schedule your tasks at the beginning of each week to save yourself the effort of deciding what to work on and when.
Break large assignments down into smaller daily and weekly tasks.
Practice turning down tasks that don’t support your goals and well-being.
Communicate often with your professors and ask for guidance.
Schedule time each day for activities that help you recharge, even for a few minutes.
Connect with your peers to socialize, collaborate on projects, and support one another.
Get regular physical activity, eat nutritious meals, and avoid relying on alcohol or drugs to manage stress.
Set work-life boundaries, and avoid dwelling on your schoolwork when spending time with your family or loved ones.

Mental health resources for graduate students

If you experience stress or other concerns during grad school, you might wonder where to find help. Resources are available for managing mental health challenges. If you’re not sure where to turn, consider starting with the following options:

Your advisor: Graduate programs often require students to have a faculty advisor. Your advisor may offer advice, support, and strategies for managing your workload.
Your student health center: Some colleges offer on-campus mental health support. You may be able to book counseling sessions through your student health website, although appointments may not always be available right away.
Your employer: If you’re working part-time during grad school, you may be able to access counseling through employee assistance programs (EAPs) or your employee health insurance.
Mental health hotlines: If you are seeking immediate support, you can contact 24-hour helplines like the National Suicide Prevention Lifeline or the Crisis Text Line .

Other support options

Some US adults with unmet mental healthcare needs don’t seek treatment due to cost . For graduate students facing high tuition costs, online therapy may be a more affordable way to get care. Platforms like BetterHelp can be more affordable, often costing under $100 a week. In addition, online platforms provide unique resources included in each member’s subscription, such as support groups, journaling prompts, worksheets, and goal-tracking features.

Studies show that online therapy may improve graduate students’ mental health. In a 2021 study, 263 graduate students participated in a brief online program to support their mental health during the COVID-19 pandemic. The majority found the program helpful and saw improvements in their sense of control.

If you’re interested in taking your studies further after getting your bachelor’s degree, you might decide to pursue a master’s degree, doctorate, or postgraduate certificate. These programs tend to be more intimate, specialized, and research-focused than undergraduate studies. However, students in these programs may face unique challenges related to finances, balancing responsibilities, conducting research, and teaching classes. These challenges may contribute to higher levels of stress, anxiety, and depression. Structuring your time, setting boundaries, practicing self-care, and getting support from your advisor or a mental health professional may help you improve your well-being during graduate school. Consider contacting a therapist online or in your area to receive tailored guidance.

What To Do After College: Figuring Out Your Professional Life Medically reviewed by Andrea Brant , LMHC
How To Make Friends In College And Build Positive Connections Medically reviewed by April Justice , LICSW
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A quantitative assessment of the views of mental health professionals on exercise for people with mental illness: perspectives from a low-resource setting

Davy vancampfort.

1 KU Leuven Department of Rehabilitation Sciences, Leuven, Belgium

2 KU Leuven, University Psychiatric Center KU Leuven, Leuven-Kortenberg, Belgium

Robert Stanton

3 Central Queensland University, School of Health. Medical and Applied Sciences, North Rockhampton, Australia

Michel Probst

Marc de hert, ruud van winkel.

4 KU Leuven, Centre for Contexual Psychiatry, Leuven, Belgium

Inez Myin-Germeys

Eugene kinyanda.

5 MRC/UVRI, Uganda Research Unit on AIDS, Entebbe, Uganda

6 Department of Psychiatry, Makerere College of Health Sciences, Kampala, Uganda

7 Senior Wellcome Trust Fellowship, London

James Mugisha

8 Butabika National Referral Mental Hospital, Kampala, Uganda

9 Kyambogo University, Kampala, Uganda

Exercise is nowadays considered as an evidence-based treatment modality in people with mental illness. Nurses and occupational therapists working in low-resourced mental health settings are well-placed to provide exercise advice for people with mental illness.

We examined the current exercise prescription practices employed by Ugandan health care professionals when working with people with mental illness, and identified perceived barriers to exercise prescription and exercise participation for people with mental illness.

In this study, 31 Ugandan health care professionals 20 men; 31.2 ± 7.1 years completed the Exercise in Mental Illness Questionnaire- Health Professionals Version EMIQ-HP.

The vast majority of the respondents 29/31, 94% reported they prescribed exercise at least “occasionally” to people with mental illness. Exercise-prescription parameters used were consistent with those recommended for people with mental illness. Regarding barriers to exercise participation, coping with side effects of psychotropic medication at the individual level and reducing stigma at community level should be prioritized.

A health care reform to enable collaboration with exercise professionals, such as exercise physiologists or physiotherapists, might increase exercise uptake for people with mental illness, thereby improving health outcomes for this vulnerable population.

Introduction

Mental illness is the leading cause of years lived with disability YLD in sub-Saharan Africa SSA, accounting for about one fifth of all disability-associated burden YLD 1 . It is estimated that the burden will more than double by 2050 Institute for Health Metrics and Evaluation, 2013. The consequences of the rising and devastating burden of mental illness is not only having an impact on the individual but also on the family and community as a whole. The quality of life of those affected is severely reduced and economic costs are significant 2 . Moreover, physical co-morbidities 3 , 4 , chronic pain 5 , 6 and HIV/AIDS 7 , 8 are more common in people with mental illness and add to the disability and burden. Despite this tremendous burden, most SSA countries invest less than 1% of the total health budget on mental health World Health Organization, 2011. As a result, mental health services are poorly resourced and considered inaccessible 9 . Therefore, it is not a surprise that treatment rates for people with mental disorders remain low, with less than 10% receiving mental health care 10 .

Despite significant efforts by the Ugandan Ministry of Health to improve access to mental health services 11 , treatment gaps remain, in part, due to the cultural beliefs and help-seeking behaviors of the Ugandan population, who often seek traditional medicine as first-line intervention, as opposed to Westernized care 9 . Community-based rehabilitation, psychoeducation and social support are recommended for low resource settings such as Uganda, with assertive community care and cognitive behavioral therapy recommended as additions in higher resourced settings with stronger service-delivery platforms 12 .

In recent years there has been an increasing interest in exercise as a stand-alone or complementary treatment modality for people with mental illnesses such as depression 13 , schizophrenia 14 , bipolar disorders 15 , alcohol use disorders 16 , post-traumatic stress disorder 17 and anxiety disorders 18 . Exercise supports patients in managing their psychiatric symptoms, andit improves the physical health and quality of life 19 . Since exercise may be implemented at low cost and often requires no or minimal resources and can be easily tailored to accommodate co-morbidities or injuries, it may be attractive in low resource settings. The potential role of exercise interventions however seems to be given low priority and to be neglected in these low resource settings 20 . This is not surprising since the emphasis in health service delivery in SSA is based on the biomedical model as opposed to the biopsychosocial model with an important focus on pharmacotherapy in the management of mental disorders Mugisha, 2016.

Although physio-therapists and exercise physiologists are ideally placed to deliver exercise interventions 21 – 24 , these clinical roles are currently not available in many low resource settings in SSA countries 20 . In clinical practice, the existing staff including mental health nurses, occupational therapists, psychologists, doctors and psychiatrists, are currently better placed to deliver exercise counseling for people with mental illness.Qualitative 25 and quantitative Stanton et al., 2015 a studies in high income countries suggest that nurses working in mental health settings acknowledge the value of exercise for people with mental illness and believe providing exercise advice is part of their role. However, such data is lacking in low resource settings, but are urgently needed in order to influence policy and practice and maximize access to the therapeutic potential of exercise at all levels of care. Such data may also help address personal factors including low confidence and limited training in exercise prescription 26 , and systemic barriers such as competing work priorities 27 , 28 that limit the provision of exercise programs for people with mental illness in low resource settings.

Therefore, in order to better inform the development of exercise interventions that can be implemented in low resource settings at all levels of care in and to define specific training needs, a comprehensive assessment of the current knowledge, attitudes, beliefs and behaviors of health practitioners working in mental health settings regarding the prescription of exercise for people with a mental illness is required. The aims of the present study are twofold. Firstly, to examine the current exercise prescription practices employed by Ugandan health care professionals when working with people with mental illness. Secondly, to identify perceived barriers to exercise prescription and perceived barriers to exercise participation for people with mental illness.

Study design

This was a cross-sectional study.

Study setting and procedure

This study was a cross-sectional investigation undertaken at Uganda's only psychiatric hospital, the Butabika National Referral Mental Hospital. The nurses and occupation therapists working in two adult long-term care units were invited to participate. Combined, these two units could accommodate 110 in-patients and employs 32 nurses and 2 occupational therapists. First, all the nurses and occupational therapists were provided with an information sheet outlining the purpose of the study and with the questionnaire. The information sheet and questionnaire were provided by a research nurse who was not working in the two adult long-term care units. There were no exclusion criteria. The information sheet stated that the research nurse was available upon request to assist in the completion of the questionnaire. After one month, the research nurse gave a one-time reminder to the staff members who volunteered to participate, to complete the questionnaire within the following month. A self-administered questionnaire were used to collect data since the participants were fluent and competent in English. Content validity, conceptual equivalence and cultural sensitivity were also not an issue. No incentive for completion of the survey was offered. Participation was anonymous with questionnaires placed in a sealed box not observable to other staff members. Informed consent was assumed on completion and return of the survey. Data were collected during November and December, 2017. Ethical clearance for the study was received from the local Butabika Hospital Research Committee.

Study instrument

Participants completed the Exercise in Mental Illness Questionnaire- Health Professionals Version EMIQ-HP for which content validity and test-retest reliability have previously been established 29 . The instrument comprises six domains of exercise knowledge, exercise beliefs, exercise prescription behaviors, barriers to exercise, personal exercise habits and demographics. Time to complete the paper-based survey was approximately 20 minutes. Exercise-prescription practices were determined using the question; “Do you prescribe exercise to people with a mental illness?” with four response options of: “Never”, “Occasionally”, “Most of the time” and “Always”. Self-rated knowledge and confidence to prescribe exercise for people with mental illness were assessed using Likert-response questions, 1 = “Very poor”, and 5 = “Excellent”. To examine the views of other well-established treatment strategies for mental illness, respondents were asked to rate how valuable they believed each treatment was, compared to exercise, using a five-point Likert scale where 1 = “Significantly less than exercise”, and 5 = “Significantly better than exercise”. Electroconvulsive therapy and bright light therapy were removed from the list of well-established treatments, as they are not practiced in the setting we investigated. Respondents then answered questions regarding strategies used to prescribe exercise including the frequency, intensity, duration, and type of exercise duration, frequency, using fixed response options. Level of agreement questions using a five-point Likertscale with anchors from 1 = “Strongly disagree” to5 = “Strongly agree” were used to examine respondents' views regarding barriers to exercise prescription for people with mental illness, and exercise participation by people with mental illness. Future training needs were examined with respect to level and topics of interest for professional development. Responses to statements for each subsection were then summed, thus a higher score indicates a higher level of agreement. Finally, the following demographic data were captured as part of the EMIQ-HP: gender male / female, age years, current marital status married or not married, years in profession, and full time employment yes or no.

Statistical analysis

Participant demographics, exercise prescription practices and responses to statements regarding barriers to exercise prescription for; and barriers to exercise participation by people with mental illness are reported using descriptive statistics mean ± SD, frequencies. In accordance with previous studies 29 , 30 , responses to statements were collapsed to three categories; “Agree”, “Neutral”, and “Disagree”. Since the Likert scale responses are not assumed to be on an equal interval scale, and frequency of responses to “Strongly agree” and “Agree” are low, these responses were collapsed to “Agree”. Based on rating scale optimization, collapsing the positive responses “Strongly agree” and “Agree” into one category is logical and does not create an artificial new category. Similarly, combining negative responses “Strongly disagree” and “Disagree” demonstrates the strength of these responses, compared to neutral and positive responses 31 .

Participants

Thirty-one health care professionals, representing 91% of potential respondents completed the EMIQ-HP. Respondents included 10 nurses, 19 nurses with specialist mental health nursing qualification and 2 occupational therapists. The characteristics of respondents are shown in Table 1 .

Demographic characteristics of respondents n=31

Characteristic	Mean ± SD	Range
Age years	31.2 ± 7.1	22 – 48
Years in profession	7.3 ± 7.1	< 1 – 34



Gender male	20	64%
Marital status married	21	68%
Full time employment yes	27	88%

Frequency of exercise prescription

Three respondents 10% reported ‘Always’ prescribing exercise, 3 (10%) reported prescribing exercise ‘Most of the time’, 23 (74%) reported prescribing exercise ‘Occasionally’ and two 6% reported ‘Never’ prescribing exercise.

Knowledge about and confidence regarding exercise prescription

Sixteen respondents (52%) indicated that they had a formal training in exercise prescription. The mean ±SD response for self-reported knowledge and confidence scores was3.6± 0.6 and 3.6±0.5, respectively. Nine respondents 29% reported a “Good” or “Excellent” knowledge of exercise prescription for mental illness. Similarly, 10 respondents (32%) reported that they are confident at prescribing exercise for people with mental illness to be ''Good” or “Excellent”.

Views of health care professionals comparing established treatments to exercise for the treatment of mental illness

Overall, between 74% and 90% of respondents believed other treatment modalities to be of equal or greater value compared to exercise. The majority of respondents n=24, 77% believed medication is ‘Somewhat’, or ‘Significantly’ more valuable than exercise. Between 45% and 65% of respondents believed other treatment modalities are ‘Somewhat’, or ‘Significantly’ more valuable than exercise. Slightly more than one-third n=11, 35% of respondents believed social skills training is of equal value to exercise. A summary of the findings regarding the value to treatments compared to exercise is shown in Table 2 .

Comparison of established treatments to exercise for the treatment of mental illness

	Significantly less than exercise	Somewhat less than exercise	Of equal value to exercise	Somewhat better than exercise	Significantly better than exercise
Medication [n,%]	1 3%	4 13%	2 6%	8 26%	16 52%
Social support [n,%]	1 3%	7 22%	9 29%	11 35%	3 10%
Family therapy [n,%]	0 0%	6 19%	9 29%	11 35%	5 17%
Social skill training [n,%]	0 0%	3 10%	11 35%	13 42%	4 13%
Cognitive behavioural therapy [n,%]	0 0%	6 19%	5 17%	15 48%	5 17%
Vocational rehabilitation [n,%]	0 0%	3 10%	8 26%	8 26%	12 38%

Exercise prescription strategies

When considering the strategies used to prescribe exercise to people with mental illness, personal discussion, including the development of an individualized program was the most frequently used strategy n=19/29, 65%. Only one respondent indicated referral to an exercise physiologist / physiotherapist for exercise prescription. The most commonly reported recommendation for exercise frequency was to exercise “As often as they can” n=12/29, 41% followed by on “Most days of the week” n=10/29, 34%. The most frequently recommended exercise intensity for people with mental illness was “At a level that makes them feel good” n=9/29, 31%, followed by “Moderate” n=7/29, 24%. “30 minutes per day'' n=11/29, 38% was the most frequently prescribed exercise duration followed by “Exercising as long as they can” n=7/29, 24%. Relaxation exercises such as yoga or Tai Chi n=16/29, 55% were the most commonly prescribed mode of exercise followed by aerobic exercise n=10/29, 34%.

Barriers to exercise prescription

Responses to statements regarding the barriers to exercise prescription for people with mental illness are shown in Table 3 . When collapsed to categories of ‘Agree’, ‘Neutral’, and ‘Disagree’, just over half n=18, 58% agreed that patient's mental health makes it impossible for them to participate in exercise. Almost half n=13, 45% agreed that getting injured during exercise is a concern. Overwhelmingly however, 87% of respondents n=27 agreed that exercise will be beneficial, and were interested in exercise prescription for this population. Only 13% n=4 agreed that exercise prescription is not part of their job, but 16% agreed that they did not know how to prescribe exercise for people with mental illness. Importantly, 71% n=22 agreed that exercise prescription for people with mental illness is best delivered by an exercise professional.

Level of agreement [n %] with statements regarding barriers to exercise prescription for people with mental illness

	Strongly disagree	Disagree	Neither disagree / agree	Agree	Strongly agree
Their mental health makes it impossible for them to participate in exercise	5, 17%	4, 13%	4, 13%	10, 32%	8, 26%
I'm concerned exercise might make their condition worse	3, 10%	15, 48%	4, 13%	8, 26%	1, 3%
I am not interested in prescribing exercise for people with a mental illness	6, 19%	21, 68%	1, 3%	1, 3%	2, 6%
I don't believe exercise will help people with a mental illness	7, 23%	20, 64%	2, 6%	1, 3%	1, 3%
Their physical health makes it impossible for them to participate in exercise	4, 13%	17, 55%	3, 10%	3, 10%	4, 13%
I'm concerned they might get injured while exercising	4, 13%	7, 23%	7, 23%	11, 35%	2, 6%
People with a mental illness won't adhere to an exercise program	3, 10%	10, 32%	7, 23%	7, 23%	4, 13%
My workload is already too excessive to include prescribing exercise to people with a mental illness.	7, 23%	17, 55%	1, 3%	5, 17%	1, 3%
Prescribing exercise to people with a mental illness is not part of my job	5, 17%	19, 60%	3, 10%	0, 0%	4, 13%
I do not know how to prescribe exercise to people with a mental illness	2, 6%	19, 60%	5, 17%	4, 13%	1, 3%
Prescription of exercise to people with mental illness is best delivered by an exercise professional.	3, 10%	3, 10%	3, 10%	14, 45%	8, 26%

Barriers to participation

The agreement with statements expressed by people with mental illness regarding exercise participation is shown in Table 4. In a manner similar to the responses to statements regarding barriers to exercise prescription, scale optimization was performed to result in three categories. When collapsed to categories of “Agree”, “Neutral”, and “Disagree”, almost three-quarters of respondents n=23, 74% agreed with the consumer view that “There is too much stigma attached to having a mental illness.” while more than half n=18, 58% agreed with the statement “There are too many side effects from the medications.”

Training needs for health care professionals

Participants were cognizant of the need for ongoing professional development in the field. More than two-thirds of respondents 23/31 indicated they would “Definitely” attend further training for exercise prescription for people with mental illness, with the most commonly reported topics of interest being “How to assess the patients' suitability for physical activity?” n=22, 71% and “How to get and maintain motivation in people with mental illness?” n=18, 58%.

General findings

The present study is the first to provide new insight from the perspectives of health professionals working in a long-term adult inpatient mental health facility in a low resource country, with regard to the prescription of exercise to people with mental illness. The 31 respondents in the present survey represent approximately 90% of the health care professionals working in the mental health setting explored.

The vast majority 29/31, 94% reported that they prescribed at least “occasionally” exercise to their patients. The positive attitude of nurses and occupational therapists towards exercise is in line with previous research in other parts of the world. For example, a British study 32 reported that 77% of mental health nurses felt that providing exercise advice and referring to a community facility was part of their role while in an Australian study Stanton et al. 33 , 2015b 72% of the nurses reported prescribing exercise to mental health consumers.

Participants self-reported a high level of knowledge and confidence in prescribing exercise for people with mental illness. This high level of knowledge is also reflected in the exercise-prescription parameters for exercise frequency, intensity, duration, and type recommended by respondents. These are consistent with those recommended for people with mental illness 33 , 34 . International guidelines call for aerobic exercise to be performed 3 to 5 days per week for 30 min at low-to-moderate or self-selected intensity 33 , 34 . The popular view regarding exercising at a level that makes them feel good, and for as long as they like, is consistent with the use of autonomous regulation in exercise prescription for people with mental illness 35 – 37 and consistent with approaches used in other health professional groups 38 , 39 .

The high level of knowledge and confidence in prescribing exercise for people with mental illness is perhaps unsurprising given that more than half of the existing work staff indicated that they are trained in exercise prescription and implementing lifestyle interventions for people with chronic or complex health conditions, a rate which is for example much higher than in Australia where only 11% of the nurses reported having any formal training in exercise prescription 38 . Since Butabika Hospital is a national referral hospital, many of its staff are likely also more exposed to information related to exercise compared to work staff in more rural areas, owing to the fact that the hospital runs a bigger budget, from both local resources and donors for continued medical education 27 , 28 . On the other hand, almost 75% of the respondents indicated that they would “definitely” attend further training for exercise prescription for people with mentalillness, in particular related to how to assess patients and how to motivate them towards an active lifestyle. More than seventy percent of the participants also reported that exercise to people with mental illness is actually best delivered by an exercise professional, although only one respondent referred patients to such an exercise professional. A potential reason for the very low referral rate is the lack of exercise specialists working in mental health care settings in low income countries 20 . It is likely that due to the strong biomedical focus on pharmacotherapy 27 , policy makers are yet to be fully aware of the benefits of including exercise specialists in the Ugandan mental health care system. Hence, a need to re-orient the current health care system including policy makers to embrace these professions in the management of mental health problems is needed. Internationally, exercise physiologists 24 and physiotherapists 21 are the health professional groups with expertise in exercise prescription for people with mentalillness. Both health professional groups are trained in exercise prescription for people with chronic and complex health conditions including for those with mental illness. Thus, exercise professionals are able to develop and deliver cost- and resource-efficient exercise interventions. To date, however, few people in Uganda, and Sub-Saharan African as a whole with mental illness are referred to exercise specialists in primary health care settings 27 , 28 . One of reasons might be the lack of mental health training for these exercise professionals in this part of the world 20 .

Despite the fact that the respondents reported a high level of knowledge and confidence in prescribing exercise for people with mental illness, the potential of exercise within the multidisciplinary treatment seems not yet to be fully endorsed in low resource countries. “Standard treatments” for mental illness were generally perceived as of greater therapeutic value to exercise. One reason might be the previously reported strong biomedical focus, while clinicians tend to favor interventions related to their own discipline, for example occupational therapists favor vocational rehabilitation and social skills training while nurses favor family support. Another issue might be the socio-cultural views of mental illness whereby potential patients do not routinely seek treatment due to the high levels of stigma, and where treatment is provided traditionally through non-Westernized approaches. Thus, exercise as part of any treatment strategy is largely underutilized.

In the current study, we also explored barriers to exercise prescription for health care professionals and participation by mental health consumers. A previous study in physical therapists demonstrated that a-motivation by mental health consumers is the most significant barrier to exercise participation 40 , while barriers to exercise prescription by nurses working in mental health in Australia extend to the systemic level 41 . For example, previous research highlighted how the fragmentation of roles, prioritization of other tasks, lack of time, and limited resources impact on the prescription of exercise by nurses working in mental health in Australia 41 . Surprisingly, in our study these barriers were not endorsed by more than half of our respondents. In our study, respondents agreed with a number of statements regarding barriers to exercise participation proposed by people with mental illness. This was especially the case for statements located more at the individual level such as the side effects from pharmacotherapy, and at the community level where stigma and negative attitudes surrounding mental illness were considered a major barrier for people with mental illness to engage in exercise. Therefore, in order to facilitate exercise uptake, deliberate efforts need to be undertaken within the hospital to assist patients in coping with the side effects of their pharmacotherapy while at the community level public health campaigns are needed to reduce the stigma associated with mental illness. These changes should be augmented by professional development opportunities suggested by respondents including the assessment, initiation, and motivation for continued exercise participation by people with mental illness.

Limitations

The present study should be considered in the light of some limitations. First, we were not able to obtain completed surveys from all health care professionals working in mental health setting where the study was undertaken. This could be due to the time commitment required, personal concerns regarding the knowledge related to exercise for people with mental illness and the lack of incentive for participation. Uganda also has a small mental health workforce with around 28 psychiatrists and 230 mental health nurses, most of whom work at Butabika 42 , thus competing priorities may have affected the survey response rate. However, considerable effort was directed towards recruitment and the proportion of professionals who completed surveys represents approximately 90% of the eligible staff. Second, the present survey was also limited to only one hospital. Butabika is however the only national mental health referral center in Uganda. Together with a small cohort, the generalizability of our findings remains to be confirmed while interdisciplinary comparisons were not possible. Third, although the EMIQ-HP has been validated before in Australia 29 , the validity for the mental health workforce in low income countries is unknown.

The present findings suggest nurses and occupational therapists who participated in this study are supportive of exercise, and those who prescribe exercise do so in accordance with accepted protocols. Moreover, respondents disagree with many of the commonly cited barriers to exercise prescription and participation in the current literature. Regarding barriers to exercise participation, reducing stigma at community level should be prioritized. Collaboration with exercise professionals such as exercise physiologists and physiotherapists as part of a multidisciplinary approach to mental health care could increase exercise uptake and consequently improve health outcomes for mental health consumers. Further examination in larger cohorts including all relevant healthcare disciplines will progress our understanding of the delivery of exercise for people with mental illness in low resourced settings.

Acknowledgements

The authors would like to thank the nurses of the Butabika National Referral and Mental Health Hospital who completed the questionnaires for the purpose of this study.

Conflict of interest

None to declare from either author.

This research was funded by Geestkracht VZW.

Role of funding source

The funding organization had no role in the research at any stage, nor influenced the decision to publish the article.

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Factors that influence mental health of university and college students in the UK: a systematic review

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