what is a quantitative nursing research article

Nursing Research (NURS 3321/4325/5366)

• Introduction
• Understand What Quantitative Research Is
• Understand What Qualitative Research Is
• Sage Methods Map
• Step 1: Accessing CINAHL
• Step 2: Create a Keyword Search
• Step 3: Create a Subject Heading Search
• Step 4: Repeat Steps 1-3 for Second Concept
• Step 5: Repeat Steps 1-3 for Quantitative Terms
• Step 6: Combining All Searches
• Step 7: Adding Limiters
• Step 8: Save Your Search!
• What Kind of Article is This?
• PICO Keyword Search Strategy
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What is Quantitative Research?

Quantitative methodology is the dominant research framework in the social sciences. it refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . quantitative research gathers a range of numeric data. some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘on a scale from 1 to 10, how depressed did you feel last week’). the collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the usa has a higher gross domestic product than spain). quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies..

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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What is Quantitative Research?

"Quantitative research is a systematic process used to gather and statistically analyze information that has been measured by an instrument. Instruments are used to convert information into numbers. It studies only quantifiable concepts (concepts that can be measured and turned into numbers)." It examines phenomenon through the numerical representation of observations and statistical analysis.

Langford, R. ( 2000). Navigating the Maze of Nursing Research . Elsevier.

Tips for Finding Quantitative Articles with a Keyword Search

If you want to limit your search to quantitative  studies, first try "quantitative" as a keyword, then try using one of the following terms/phrases in your search (example: lactation AND statistics):

Correlational design*

Effect size

Empirical research

Experiment*

Quasi-experiment*

Reliability

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General Nursing Research Guide

• Synonyms, Related Terms and Boolean Operators
• Boolean Operators and Modifiers
• Linfield Library Catalog
• Finding Quantitative, Qualitative, Primary and Secondary Sources
• Evaluating Sources

Quantitative and Qualitative

• What is Quantitative and Qualitative Research?

Quantitative research is a methodology that relies on the exploration of numerical patterns, and it can be precisely measured. Using scientific inquiry, quantitative research  relies on data that are observed or measured  to examine research questions.  Randomized Controlled Trials (RCT), Systematic Reviews, and Meta-Analysis  are all examples of Quantitative research.  

Qualitative research refers to any research based on something that is impossible to accurately and precisely measure. It focuses on the "why" rather than the "what" and relies on the direct experiences of human beings as meaning-making agents in their every day lives.  Case studies, Ethnography, Grounded Theory, Narrative Studies or Focus Groups , are all examples of Qualitative research. 

• Finding Quantitative and Qualitative Research in PubMed and CINAHL?

The best way to find quantitative and/or qualitative articles in either PubMed or CINAHL is to search by publication type.

For quantitative articles, select  Randomized Controlled Trials (RCT), Systematic Reviews, or Meta-Analysis  in either  CINAHL  or  PubMed. 

For qualitative, search for  Case Studies, Observational, Personal Narratives  in  PubMed , and  Anecdote, Case Study, Editorial, Interview, Meta Synthesis  in  CINAHL . 

You can also search both PubMed and CINAHL using 'quantitive' or 'qualitative' as part of your keyword search, but it's usually more effective to look for a specific publication type. 

Primary and Secondary Research

• What is Primary Research?

Primary research   in nursing is one that reports the original findings of a study or experiment. It is usually written by the person(s) conducting the research, and is often found in peer reviewed journals. 

Sources of primary research include:

Case studies

Clinical trials or randomized clinical trials (RCT)

Cohort studies

Dissertations or theses

Survey research

• What is Secondary Research?

S econdary Research  is one that summarizes, synthesizes or comments on original research. The Author(s) describe research done by others.

Sources of secondary research include:

Clinical practice guidelines

Meta-analysis

Patient education material

Reviews of literature

Systematic reviews

• Finding Primary Research in PubMed and CINAHL

To find primary research in PubMed and CINAHL, you'll need to filter by article type. Trying searching for Clinical Trials, Randomized Controlled Trials, or Case Study/Reports in either database to find primary research. 

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Measurement in Nursing Research

Curtis, Alexa Colgrove PhD, MPH, FNP, PMHNP; Keeler, Courtney PhD

Alexa Colgrove Curtis is assistant dean and professor of graduate nursing and director of the MPH–DNP dual degree program and Courtney Keeler is an associate professor, both at the University of San Francisco School of Nursing and Health Professions. Contact author: Alexa Colgrove Curtis, [email protected] . Nursing Research, Step by Step is coordinated by Bernadette Capili, PhD, NP-C: [email protected] . The authors have disclosed no potential conflicts of interest, financial or otherwise. A podcast with the authors is available at www.ajnonline.com .

Editor's note: This is the fourth article in a series on clinical research by nurses. The series is designed to give nurses the knowledge and skills they need to participate in research, step by step. Each column will present the concepts that underpin evidence-based practice—from research design to data interpretation. The articles will be accompanied by a podcast offering more insight and context from the authors. To see all the articles in the series, go to https://links.lww.com/AJN/A204 .

Quantitative research examines associations between research variables as measured through numerical analysis, where study effects (outcomes) are analyzed using statistical techniques. Such techniques include descriptive statistics (for example, sample mean and standard deviation) and inferential statistics, which uses the laws of probability to evaluate for statistically significant differences between sample groups (for example, t test, ANOVA, and regression analysis). Qualitative research explores research questions through an analysis of nonnumerical data sources (for example, text sources collected directly or indirectly by the researcher) and reports outcomes as themes or concepts that describe a phenomenon or experience.

As described in the first installment of this series, “a common goal of clinical research is to understand health and illness and to discover novel methods to detect, diagnose, treat, and prevent disease”; with this in mind, research questions must “focus on clear approaches to measuring or quantifying change or outcome,” the research outcome being the “planned measure to determine the effect of an intervention on the population under study.” 1

In this article, we explore measurement in quantitative research. We will also consider the concepts of validity and reliability as they relate to quantitative research measurement. Qualitative analysis will be considered separately in a future article in this series, as this methodology does not typically use a prescribed mechanism for measurement of research variables.

DEFINING THE VARIABLE OF INTEREST

Measurement in research begins with defining the variables of interest. Often, researchers are interested in exploring how variation in one factor or phenomenon influences variation in another. The dependent variables (outcome variables) in a study reflect the primary phenomenon of interest and the independent variables (or explanatory variables) reflect the factors that are hypothesized to have an impact on the primary phenomenon of interest (the dependent variable). 2 For example, a researcher might rightly hypothesize that body mass index (BMI) influences blood pressure, further hypothesizing that increases in BMI are associated with increases in blood pressure. In a study testing this hypothesis, blood pressure is the dependent variable and BMI is an independent variable.

In identifying the variables of interest in a study, researchers are likely to have ideas of concepts they would like to explore. For instance, among other things the researcher is interested in in the above example is weight. A conceptual definition of a research variable provides a general theoretical understanding of that variable; regarding weight, a person might be considered “thin” or “overweight.” Nevertheless, in moving from theory to practice, the researcher must consider how to operationalize this theoretical definition—that is, the researcher needs to select specific mechanisms for measuring the proscribed variables conceptualized in the study. Thus, an operational definition provides a measurable definition of a variable. Continuing with the above example, BMI would be a means of operationalizing the weight variable, where a person with a BMI of 25 or above is categorized by the Centers for Disease Control and Prevention as overweight. 3 In operationalizing variables, first look in existing evidence-based literature, practices, and professional guidelines. For instance, the researcher might consider measuring depression using the validated and widely utilized Patient Health Questionnaire-9 (PHQ-9) depression assessment scale or assessing longitudinal hyperglycemic risk by using the accepted measurement of glycated hemoglobin (HbA 1c ) level.

MEASUREMENT TOOLS

Researchers rely on measurement tools and instruments to create quantitative assessments of the variables studied. In some cases, direct measurements can be made using biometric measurement instruments to collect physiologic data such as weight, blood pressure, oxygen saturation level, and serum laboratory values. These biometric assessments are considered direct measures . 4 To quantify more abstract concepts, such as mood states, attitudes, and theoretical concepts like “caring,” researchers must consider less obvious proxy measures. Proxy measures constructed to quantify more abstract concepts are considered indirect measures . 4 For instance, Hughes developed an instrument to assess peer group caring during informal peer interactions among undergraduate nursing students. 5 While unable to directly measure the theoretical concept of caring, Hughes was able to construct an indirect proxy assessment using a survey tool.

Indirect, and even direct, measures can be operationalized in several ways. For instance, a researcher may consider operationalizing the concept of depression using the PHQ-9 depression assessment scale, using the Center for Epidemiological Studies Depression scale, or by applying Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition , diagnostic criteria. The study findings may be affected by how a variable is operationalized and which measurement tools are utilized; therefore, researchers should give serious thought to study objectives, sample/target populations, and other important considerations when operationalizing a variable. More specifics on measurement formats and methods of administration will be explored in the next installment of this series.

LEVELS OF MEASUREMENT

Levels of measurement describe the structure of a variable (see Table 1 ).

Nominal level . The lowest form of measurement, the nominal level groups data into distinct and exclusive categories that cannot be rank ordered. 2 Gender identity, race/ethnicity, occupation, geographic location, and clinical diagnoses are all examples of categories that contain nominal level data. This type of variable may also be referred to as a categorical variable. 6

Ordinal level data can also be categorized into distinct and exclusive groups; however, unlike nominal data, ordinal data can be ordered by rank. Likert-type scale variables reflect a classic example of ordinal level data, where responses can be rank ordered by “strongly disagree,” “disagree,” “neutral,” “agree,” and “strongly agree.” The 0-to-10-point pain scale is another example of the ordinal level of measurement. Using this scale, a patient provides a subjective determination of the experience of pain, where 0 reflects no pain and 10 reflects the highest pain threshold. As with all ordinal data, the precise quantitative distance between the descriptor data points is impossible to assess—the differences between a pain determination of 3 and one of 4 and a pain determination of 7 and one of 8 cannot be precisely calculated. Further, the distance between each pain level (for example, jumping from a pain level of 3 to 4 or from a pain level of 7 to 8) is not assumed to be incrementally or objectively equal. 2 Despite these drawbacks, ordinal level data are frequently translated into a numerical expression so they can be analyzed as interval or ratio data. For example, a Likert scale can be translated into a scale ranging from “strongly disagree = 1” to “strongly agree = 5,” allowing for the calculation of a numerical mean satisfaction score.

Interval level data reflect a chronological sequencing of data points with distances that are assumed to be quantifiable and equal in magnitude, such as ambient temperature. As with ambient temperature measured in degrees Fahrenheit, the magnitude of the chronological difference between each data point is assumed to be equal along a continuum of continuous values. Of note, interval data do not include a true and meaningful zero, the total absence of the characteristic being measured. 2 For example, there is no such thing as the absence of temperature.

Ratio level data provide the final and most robust level of measurement. Ratio level data are measured continuously, with equal spacing between intervals and with a true zero. Examples include height, weight, heart rate, and serum laboratory values. A zero value is interpreted as the absence of the characteristic. Once again, researchers should be cautious in defining how a variable is operationalized because the level of measurement will influence the types of statistical analyses that can be performed in the evaluation of study outcomes. Interval and ratio levels of measurement result in the most robust statistical analyses and research results. Statistical analysis techniques will be discussed in more detail later in this series.

MEASUREMENT ERROR

For variables to provide a meaningful and appropriate representation of the underlying concept being measured, data measurement needs to be accurate and precise. Measurement error reflects the difference between the measured and true value of the underlying concept. The value of an individual measurement can be described as follows 7 :

Chance error (or random error) changes from measurement to measurement, while bias (or systematic error) influences “all measurements the same way, pushing them in the same direction.” 7 Chance errors are individually unpredictable and inconsistent and in the long run should cancel each other out. If there is no bias in one's measurement, the individual and exact values should ultimately be equal. Bias, however, is inherent in all models and causes a systematic deviation from the true, underlying value.

Weight offers an excellent example of measurement error. Suppose some patients are weighed in the morning, some in the afternoon; some wear coats while others do not; some have eaten while others have fasted, and so forth. This variation reflects random error—we'd expect this positive and negative, over- and underestimation, to average out once enough patients have been sampled. Further, suppose the scale is incorrectly calibrated, such that it reports that every person weighs five pounds more than her or his actual weight. This result reflects a positive bias in the estimates and will not be corrected no matter how many patients are sampled.

The potential for bias resulting from measurement error falls broadly under the category of information bias —are researchers measuring what they think they are measuring? Information bias is present if the study data collected are somehow incorrect. 8 This can occur because of faulty measurement practices that systematically result in the under- or overvaluation of a measure, as described in the scale example above, or because of systematic misreporting by respondents. There are many forms of information bias, including recall bias, interviewer bias, and misclassification, as well as systematic differences in soliciting, recording, and interpreting information. 8 For instance, consider a study of adolescent sexual behavior in which adolescents are interviewed in the home with parents or guardians present. One might assume that adolescents in these circumstances would underreport the number of sexual partners they have had; as a result, one might expect this systematic underreporting to represent a downward bias in the data collected.

Other forms of bias exist, such as selection bias (if study participants are systematically different from the target population, or population of interest). Selection bias, for instance, does not necessarily affect the internal validity of the study (the ability to collect valid data) but may affect the external validity of the study (can researchers truly generalize findings to the population of interest?). These forms of bias will be described in further detail elsewhere in this series.

Measurement error is study and model specific; it comes in many forms and the type of error affects the level or form of bias. In interpreting results and designing research, researchers need to be aware of potential measurement error, do what they can to minimize bias, and provide a thorough assessment of bias in presenting the limitations of their work.

VALIDITY AND RELIABILITY

Validity refers to the degree to which a measurement accurately represents the underlying concept being measured. Basically, does the test operate as designed? Researchers need to consider the validity of use of the measurement instrument within the context of specific populations. For instance, in Hughes's study of caring among peer groups in undergraduate nursing populations, the author not only had to ensure that her instrument accurately measured caring among peer groups but also needed to verify that this measurement was accurate in nursing undergraduate populations. 5 In this instance, Hughes developed the survey explicitly with undergraduates in mind, making the second point easier to achieve.

Suppose, however, that a researcher wanted to use a version of the survey to gauge peer caring among nursing faculty. Would this be appropriate? Not without first assessing the validity of the survey within the new sample. The validity of an instrument can be assessed in several ways: by having the instrument reviewed by a content expert, comparing the instrument's results with those from an alternative assessment metric, assessing how well the instrument predicts current or future performance for the concept under consideration, and running a factor analysis (a statistical procedure that compares items or subscales within an instrument with each other and with the overall instrument outcome).

Reliability and validity go hand in hand. Reliability reflects the consistency of a measurement tool in reporting variable data. An instrument must be reliable to be valid. The reliability of an instrument can be gauged in three ways:

• stability (the consistency of outcomes with repeated implementation)
• interrater reliability (the consistency between different evaluators)
• internal consistency (the homogeneity of items within a scale as they relate to the measurement of the concept under investigation)

Cronbach α is a statistical procedure to assess instrument reliability by determining the internal consistency of items on a multi-item scale. 4, 9 Internal consistency evaluations examine how closely items on a scale represent the outcome concept under evaluation. Cronbach α scores range from 0.00 to 1.00—the higher the score the better the internal consistency. An acceptable Cronbach α as an evaluation of instrument reliability is often considered to be 0.70; however, a score of 0.80 or higher is preferable.

When choosing a measurement instrument for quantitative research, it is best to select one that has documented validity and reliability; alternatively, the researcher may independently complete and describe an assessment of the instrument's validity and reliability. Evaluation of a research study prior to practice implementation should also include assessment of the validity and reliability of the measurement instrument employed, which should be described within the research article.

This installment of AJN 's nursing research series explores how to measure both research outcomes and factors that are hypothesized to influence outcomes. Careful selection of measurement instruments will enhance the accuracy of research and maximize the ability of the research findings to meaningfully inform nursing practice and improve the well-being of patient populations. The next article in this series will further explore the selection and utilization of measurement instruments in the design and execution of nursing research.

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Sampling design in nursing research, selection of the study participants, interpretive methodologies in qualitative nursing research, introduction to statistical hypothesis testing in nursing research, cross-sectional studies.

Understanding Nursing Research

• Primary Research

What is Quantitative Research?

How do i tell if my article has quantitative research, qualitative research.

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There are two kinds of research: Quantitative and Qualitative

Quantitative is research that generates numerical data. If it helps, think of the root of the word "Quantitative." The word "Quantity" is at its core, and quantity just means "amount" or "how many." Heart rates, blood cell counts, how many people fainted at the jazz festival-- these are all examples of quantitative measures.

Qualitative , on the other hand, is a more subjective measurement. Think of the root of the word again, this time it's "Quality." If someone is called a quality person or someone's selling a high quality product, they're being measured in subjective terms, rather than concrete, objective terms (like numbers.) Qualitative research includes things like interviews or focus groups.

Just like when we examine whether or not our article is an example of Primary Research, the best way to examine what kind of data your article uses is by reading the article's Abstract, Methodologies, and Results sections. That will tell you how the research was conducted and what kind of data (qualitative or quantitative) was collected.

An example of what to look for in the Abstract can be seen here:

You can see that data was evaluated (66% of students were in compliance with school immunization requirements), a strategy was implemented (letters and emails were sent to student's parents/guardians), and at the end of the study, new quantitative data is reported (99.6% of students were in compliance with vaccination requirements).

Finding qualitative research can be trickier, since it can often take more time to collect. Examples of qualitative data include things like interview transcripts, focus group feedback, and journal entries detailing people's experiences and feelings. The easiest way to search for a qualitative study is to include the word "qualitative" as a keyword in your database search along with the search terms about the topic you're interested in.

Check out the video below to see an example of searching for qualitative research in CINAHL.

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Finding Quantitative or Qualitative Nursing Research Articles

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RESEARCH ARTICLES

Qualitative vs. quantitative, mixed methods research, review articles, systematic review, integrative review, meta-analysis.

A research article describes an original study that the author(s) conducted themselves.

It will include a brief literature review, but the main focus of the article is to describe the theoretical approach, methods, and results of the authors' own study.

Look at the abstract or full text of the journal article and look for the following:

• Was data collected?
• Were there surveys, questionnaires, interviews, interventions (as in a clinical trial)?
• Is there a population?
• Is there an outline of the methodology used?
• Are there findings or results?
• Are there conclusions and a discussion of the significance?

Research articles use a standard format to clearly communicate information about an experiment. A research article usually has 7 major sections:

• Introduction/Objective
• Discussion/Conclusion

A research article has a hypothesis, a method for testing the hypothesis, a population on which the hypothesis was tested, results or findings, and a discussion or conclusion.

From A Dictionary of Nursing

Mixed methods research combines quantitative and qualitative research methods in a single study. The use of mixed methods research is increasingly popular in nursing and health sciences research. This growth in popularity has been driven by the increasing complexity of research problems relating to human health and wellbeing.

• Mixed Methods Research in the Health Sciences

Review articles summarize the current state of research on a subject by organizing, synthesizing, and critically evaluating the relevant literature. They tell what is currently known about an area under study and place what is known in context. This allows the researcher to see how their particular study fits into a larger picture. Review articles are NOT original research articles. Instead, they are a summary of many other original research articles. When your teacher tells you to obtain an "original research article"or to use a primary source, do not use an article that says review. Review articles may include a bibliography that will lead you back to the primary research reported in the article.

Systematic Review  - A systematic review is conducted to answer specific, often narrow clinical questions. These questions are formulated according to the mnemonic PICO (Population, Intervention, Comparison, Outcomes). A systematic review involves the identification, selection, appraisal and synthesis of the best available evidence for clinical decision making. A properly conducted systematic review uses reproducible, preplanned strategies to reduce bias and instill rigor and pools of information from both published and unpublished sources. A quantitative systematic review uses staistical methods to combine results of multiple systems, and may or may not be a meta-analysis.

It is not unusual now to find more that one systematic review addressing the same or similar questions paving the way for meta-summary or meta-study, a systematic review of systematic review.

Encyclopedia of Nursing Research

Integrative Review  - a specific review method that summarizes past empirical or theoretical literature to provide a more comprehensive understanding of a particular phenomenon or healthcare problem. The integrative review method is an approach that allows for the inclusion of diverse methodologies and has the potential to play a greater role in evidence-based practice for nursing.  

Whittemore & Knafl, 2005

Meta-Analysis  - a quantitative approach that permits the synthesis and integration of results from multiple individual studies focused on a specific research question. The outcome of this quantitative approach for reviewing literature has tremendous potential for a practice-based discipline such as nursing.

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Differences between Qualitative & Quantitative Research

" Quantitative research ," also called " empirical research ," refers to any research based on something that can be accurately and precisely measured.  For example, it is possible to discover exactly how many times per second a hummingbird's wings beat and measure the corresponding effects on its physiology (heart rate, temperature, etc.).

" Qualitative research " refers to any research based on something that is impossible to accurately and precisely measure.  For example, although you certainly can conduct a survey on job satisfaction and afterwards say that such-and-such percent of your respondents were very satisfied with their jobs, it is not possible to come up with an accurate, standard numerical scale to measure the level of job satisfaction precisely.

It is so easy to confuse the words "quantitative" and "qualitative," it's best to use "empirical" and "qualitative" instead.

Hint: An excellent clue that a scholarly journal article contains empirical research is the presence of some sort of statistical analysis

See "Examples of Qualitative and Quantitative" page under "Nursing Research" for more information.

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Examples of Qualitative vs Quantitiative

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Difference between Qualitative vs. Quantitative Research

Qualitative research allows you to discover the why and how of people and activities. The abstract from this dissertation shows  the study: 

• Asks open-ended questions
• Examines people's activities
• Gives a narrative conclusion 
• Observations and Interviews 
• Understanding and interpreting social interactions and behaviors 
• Uses a small group of subjects 
• Uses words 

Quantitative research is based on things that can be accurately and precisely measured. The abstract shows that this dissertation:

• Describes with statistics
• Gives a statistical result 
• Makes predictions 
• Objective 
• Studies a randomly selected group 
• Tests a theory 
• Uses numbers
• Uses a survey 

Identifying Quantitative Research- Example

This abstract has several indications that this is a  quantitative  study:

• the goal of the study was examining relationships between several variables
• the researchers used statistical methods (logistic regression models)
• subjects completed questionnaires
• the study included a large number of subjects

Based on Eastern Michigan University Library Libguide Quantitative and Qualitative Research 

Identifying Qualitative Research-Example

This abstract has several indications that this is a qualitative study: 

• the goal of the study was to explore the subjects' experiences. 
• the researchers conducted open-ended interviews. 
• the researchers used thematic analysis when reviewing the interviews. 

Based on Eastern Michigan University Library Libguide  Quantitative and Qualitative Research 

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NURP 410: Advanced Research Methods: Qualitative and Quantitative Articles

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Here you will find descriptions, criteria, and examples of qualitative and quantitative literature. Once you understand the differences between qualitative and quantitative research articles, see the Database Search Tips page in this guide for help with finding the articles you need.

Qualitative vs. Quantitative

From A Dictionary of Nursing

About Qualitative Studies

Qualitative research includes all modes of inquiry that do not rely on numbers or statistical methods.

Naturalistic [qualitative] approaches comprise a wide array of research traditions, most often in the categories of ethnography, grounded theory, and phenomenology, but they also include ethnology, ethnomethodology, hermeneutics, oral and life histories, discourse analysis, case study methods, and critical, philosophical, and historical approaches to inquiry.

Learn more!   Encyclopedia of Nursing Research

Finding Qualitative Articles

Finding qualitative studies can be slightly more challenging because this type of methodology is less commonly used in nursing research.  

Try adding one of the following keywords to your search:

• qualitative studies (also a subject term)
• focus group
• grounded theory
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Quantitative research on the impact of COVID ‐19 on frontline nursing staff at a military hospital in Saudi Arabia

Loujain sharif.

1 Faculty of Nursing, King Abdulaziz University, Jeddah Saudi Arabia

Khalid Almutairi

2 King Fahad Armed Forces Hospital (KFAFH), Jeddah Saudi Arabia

Khalid Sharif

Alaa mahsoon, maram banakhar, salwa albeladi, yaser alqahtani, zalikha attar, farida abdali, rebecca wright.

3 Johns Hopkins School of Nursing, Baltimore Maryland, USA

Associated Data

The data that support the findings of this study are available from the corresponding author upon reasonable request.

The aim of the study was to examine the relationship between stress, psychological symptoms and job satisfaction among frontline nursing staff at a military hospital in Saudi Arabia during the COVID‐19 pandemic.

Descriptive cross‐sectional study.

Data were collected using an online survey. All Registered Nurses ( N  = 1,225) working at a military hospital between February to April 2021 were contacted, 625 responded (51%). Data were analysed using descriptive and multivariate analysis, Student's t‐test for independent samples and one‐way analysis of variance followed by Tukey's multiple comparison tests.

Stress was experienced more significantly than depression or anxiety. Approximately 29% of the change in scores for psychological symptoms was explained by age group, being a Saudi national and working in emergency departments ( F [3,620]  = 19.063, p  < 0.0001). A 37% change in nursing stress scores was explained by nationality and work department. ( F [5,618]  = 19.754, p  < 0.0001). A 29% change in job satisfaction scores was explained by nationality and work department ( F [3,620]  = 19.063, p  < 0.0001).

1. INTRODUCTION

Saudi Arabia reported its first case of coronavirus disease 2019 (COVID‐19) on March 2, 2020 (Reuters Staff,  2020 ; Zu et al.,  2020 ). The World Health Organization has identified the COVID‐19 outbreak as a public health emergency and global pandemic (World Health Organization,  2020 ). The impact of COVID‐19 on those who have contracted it received rapid investigation and documentation (Harper et al.,  2020 ). However, healthcare workers were quickly recognized to be experiencing a secondary impact of COVID‐19, owing to vulnerability to stressors such as inadequate resources, long shifts, sleep problems, work−life imbalances and new occupational hazards (Sasangohar et al.,  2020 ). Notably, previous research on the impact of other coronavirus syndromes (severe acute respiratory syndrome, Middle East respiratory syndrome) found that approximately 62% of healthcare workers reported general health concerns, fear, insomnia, psychological distress, burnout, anxiety, depressive symptoms, posttraumatic stress disorder, psychosomatic symptoms and perceived stigma (Sasangohar et al.,  2020 ).

Compared with other healthcare professionals, nursing staff are particularly susceptible to the negative impact of a pandemic, with a higher vulnerability to negative outcomes associated with working in high‐risk departments (Shaukat et al.,  2020 ). Moreover, the impact is not limited to psychological effects. One systematic review on estimated COVID‐19 infections and deaths among healthcare workers reported 37.2 deaths per 100 infections in nursing staff aged at least 70 years (Bandyopadhyay et al.,  2020 ). Another study conducted in the UK found that out of 157 COVID‐19‐related deaths among medical health workers, 48 (30.6%) were nurses (Kursumovic et al.,  2020 ). This combination of physical (e.g. infection transmission and the underlying manifestations) and psychological effects (e.g. burnout, stress, anxiety and depression) caused by the pandemic (Hu et al.,  2020 ) has led to substantial concerns for nursing staff, with statistically significant bearing on job satisfaction (Del Carmen Giménez‐Espert et al.,  2020 ).

2. BACKGROUND

There has been a concerted effort in Saudi Arabia to understand and mitigate the impact of COVID‐19 on nursing staff, with studies investigating stress, fear of infection and resilience in relation to COVID‐19 (Tayyib & Alsolami,  2020 ); stress and coping strategies in dealing with COVID‐19 (Muharraq, 2021); and nursing knowledge and anxiety related to COVID‐19 (Alsharif,  2021 ). However, these studies give descriptive statistics with relatively small samples of less than 300 nurses, and, to the best of our knowledge, no study has yet focused on assessing multiple psychological symptoms (depression, anxiety, and stress) collectively in relation to job satisfaction. Furthermore, the effects of COVID‐19 among nursing staff in military hospitals have not yet been explored.

This is a key setting for investigation, as military hospitals in Saudi Arabia are considered highly specialized healthcare organizations, providing all forms of health care to an exclusive population of military personnel and their family members (Walston et al.,  2008 ). Healthcare providers recruited for military hospitals must meet high standards and requirements that differ from those in non‐military care settings (Olenick et al.,  2015 ). Because of higher standards and higher pay levels compared with other healthcare organizations in Saudi Arabia, military hospitals often employ healthcare providers, and nurses in particular, from different countries worldwide (Almalki et al.,  2011 ). Despite the higher salaries and expectations of care associated with urgent needs, military hospitals have had to adapt their policies and protocols in response to greater and new patient needs as a result of COVID‐19. Therefore, these hospitals have also been impacted by the brutal reality, thereby leading to an increase in resignations among nursing staff. Probable reasons for this increase include greater workloads, mandatory overtime, withholding of annual leave and switching of nurses from less demanding areas (e.g. outpatient clinics) to more demanding care areas (e.g. inpatient units), along with the risk of contracting COVID‐19 (King Fahad Armed Forces Hospital,  2020 ). These changes suggest that nursing staff at military hospitals have experienced many of the same mental and physical side effects as nurses in non‐military hospitals, with the same consequential burnout and resignations. However, it is also commonly reported that nurses avoid seeking psychological support and services (Knaak et al.,  2017 ). This may be due to a fear of stigma and discrimination in the workplace, where needing mental health help can be perceived as weakness (Jones et al.,  2020 ), which is a phenomenon that is particularly common among military personnel (Hernandez et al.,  2014 ).

Despite investigations into the types of symptoms experienced by nursing staff as outlined above, few studies have explored the relationship between psychological impact and nurses' job satisfaction within the context of military hospitals in the Middle East. Therefore, the present study aimed to examine the relationships within and between stress, psychological symptoms (including depression and anxiety) and job satisfaction among frontline nursing staff at a military hospital in Saudi Arabia during the COVID‐19 pandemic. The purpose of this study was to identify key components that may benefit not only the study site in improving nursing staff retention but also the wider healthcare field, as nursing retention is an increasingly documented challenge. We hypothesized that the abovementioned challenges encountered by nurses, as a secondary impact of COVID‐19, are likely to be linked to low job satisfaction among frontline nurses.

3.1. Design

We used a descriptive cross‐sectional design with a quantitative questionnaire. Convenience sampling was used to recruit Registered Nurses (RNs) working in all hospital units. Overall, 1,125 RNs worked at the study site. The hospital only has full‐time RNs and does not employ part‐time or agency RNs. As such there was no criteria excluding any RN employed at the hospital from participation in this study. Five hundred seventy‐six participants were required for a 50% response rate (Sataloff & Vontela,  2021 ). Data were collected from one military healthcare organization in the western region of Saudi Arabia. The hospital provides all medical services with a 420‐bed capacity, serving members of the Saudi Arabian Armed Forces and their families. The hospital is accredited by the Central Board for Accreditation of Healthcare Institutions, Joint Commission International and International Organization for Standardization, and it is the only adult cardiac surgical facility in the western region.

3.2. Method

The questionnaire comprised four sections and was in English language, with 122 items, in total and took approximately 35 minutes to complete.

Section 1 – Demographic information : We collected data on eight items: age, gender, marital status, nationality, education level, experience and department.

Section 2 – Expanded Nursing Stress Scale (ENSS; French et al.,  2000 ): The ENSS (Cronbach's alpha = 0.96) identifies the sources and frequency of stress among hospital nurses. The scale comprises a total of 57 items on the following stressful situations: death and dying patients (7 items), conflict with physicians (5 items), inadequate emotional preparation (3 items), problems related to peers (6 items), problems related to supervisors (7 items), workload (9 items), uncertainty concerning treatment (9 items), patients and their families (8 items) and discrimination (3 items). The ENSS was also used in the present study to assess the frequency in which nurses experienced work stressors, rated within a range between 0–4, on a scale modified from the original as follows: I have not encountered it (0), never stressful (1), occasionally stressful (2), frequently stressful (3) and always stressful (4). In a pilot test of the modified ENSS, conducted by the authors of this study, the Cronbach's alpha was 0.98.

Section 3 – Depression , Anxiety and Stress Scales (DASS; Lovibond & Lovibond,  1995 ): The DASS (Cronbach's alpha = 0.89) focuses on assessing depression, anxiety and stress among hospital nurses. Each of the three scales contains seven items. The depression scale assesses dysphoria, hopelessness, devaluation of life, self‐deprecation, lack of interest/involvement, anhedonia and inertia. The anxiety scale assesses autonomic arousal, skeletal muscle effects, situational anxiety and subjective experience of anxious affect. The stress scale assesses difficulty relaxing, nervous arousal and being easily upset/agitated, irritable/over‐reactive and impatient. The DASS is rated on a scale ranging between 0–3: (0) does apply to me at all , (1) applies to me to some degree or some of the time , (2) applies to me to a considerable degree or a good part of time and (3) applies to me very much or most of the time . Cronbach's alpha for the DASS in the current study was calculated as 0.969, indicating excellent reliability.

Section 4 – Job Satisfaction Survey (JSS; Spector,  1985 ): The JSS (Cronbach's alpha 0.91) assesses job satisfaction among hospital nurses. It includes 36 items with nine facets as follows: pay (4 items), promotion, supervision (4 items), fringe benefits (4 items), contingent rewards (4 items), operating procedures (4 items), co‐workers (4 items), nature of work (4 items) and communication (4 items). Items are rated on a six‐point Likert scale with responses ranging from 1 ( disagree very much ) to 6 ( agree very much ). The JSS demonstrated acceptable reliability in the current study, with a Cronbach's alpha of 0.798. Regarding the scoring system, scores for each four‐item subscale ranged from 4 to 24 and were scored as follows: dissatisfied (4–12 points), ambivalent (12–16) and satisfied (16–24). For the total 36‐item JSS, scores ranged from 36 to 216 and were scored as follows: dissatisfied (36–108 points), ambivalent (108–144) and satisfied (144–216; Spector,  1994 ).

3.3. Data collection process

After obtaining ethical approval, potential study participants who were recruited to participate through unit meetings by the head nurses of the units, who acted as gatekeepers. All relevant information on the study, including its research topic, aim, sample and significance were explained to all RNs in each unit. Within Saudi culture, in addition to communication modalities such as email, social media platforms are a common and effective method of communicating with groups within different organizations. Therefore, the head nurse in each unit sent the survey using google form as an electronic link via the social media application “WhatsApp” to all RNs who agreed to participate in the study. The survey was sent out in February 2021 and remained available until April 2021.

3.4. Analysis

Data were analysed using SPSS 26.0 Windows version statistical software (IBM, Armonk, NY, USA). Descriptive statistics (means, standard deviations, frequencies and percentages) were used to describe the quantitative and categorical variables. Student's t‐test for independent samples was used to compare the mean values of quantitative outcome variables in relation to the categorical study variable with two categories. One‐way analysis of variance, followed by Tukey's multiple comparison tests (Tukey,  1953 ), was used to compare the mean values of quantitative outcome variables in relation to the categorical study variables with more than two categories. A p ‐value of ≤0.05 was used to report the statistical significance of the results.

For the multivariate analysis, a stepwise Multiple linear regression was carried out to observe the independent relationship of variables of categorical study variables with the three quantitative variables (DASS, ENSS and JSS scores). As the study variables were categorical, dummy variables were created to include them in the model. The proportion of variability R 2 was used to observe the change in the outcome variable explained by the significant independent variables in the model. Regression coefficients were used to observe changes in the outcome variables. A p ‐value ≤0.05, was used to report the statistical significance of the estimates.

3.5. Ethics

Ethical approval was obtained from the King Fahd Armed Forces Hospital‐ Jeddah, Research and Ethics Committee (Ref. number: REC 398), confirming no risk to study participants via the application of an anonymous online survey. The cover page of the survey provided key information, including the importance and purpose, expected time necessary to complete the survey, and why survey recipients were asked to participate. A statement regarding confidentiality and anonymity was included within the online link to the survey. No financial incentives were offered.

Of the 624 nurses who completed the survey (response rate: 51%), 91.3% were women, approximately two‐thirds (66.8%) were aged between 25–35 years, and more than 50% were unmarried. The majority were Filipino (75.8%), and only 5.6% were Saudi. Approximately 90% of the sample had a bachelor's degree, and 48.4% had 1–5 years of experience; 6.3% had more than 15 years of experience. The sample was distributed among the following departments and units: emergency departments (14.6%), intensive care units (22.6%), inpatient units (39.1%) and outpatient units (9.6%); the remaining 14.1% were from other departments. A quarter of the sample (n = 156) had tested positive for COVID‐19 (Table  1 ).

Socio‐demographic and professional characteristics of participants ( N  = 624)

Table  2 shows the mean values of the three DASS subscales (depression, anxiety and stress). The mean stress score was higher than the mean scores for either depression or anxiety. Table  3 shows the ENSS scores and mean values of its nine domains, in which the mean score of the “workload” domain was highest (2.39), followed by mean scores of “patients and their families” (2.30) and “problems relating to supervisors” (2.14); the mean scores of the remaining six domains were less than 2.0 The mean value for the nine domains of the JSS was 121.07 (22.1), which indicated ambivalence (Table  4 ). The only mean score that indicted satisfaction was in the “nature of work” domain (17.04), followed by “co‐workers” (15.88) and “supervision” (15.16). The mean scores of the remaining six domains were less than 15.0, ranging from ambivalent to dissatisfied.

Comparison of mean scores of DASS sub scales and total score in relation to socio‐demographic and professional characteristics of study subjects ( n  = 624)

Note : Bolded text denotes p value of <0.05.

Comparison of mean values of nine domains and total score of ENSS scale in relation to socio‐demographic and professional characteristics of study subjects ( n  = 624)

Comparison of mean values of nine domains and total score of job satisfaction scale in relation to socio‐demographic and professional characteristics of study subjects ( n  = 624)

4.1. Bivariate and multivariate analyses

For mean DASS scores, bivariate analysis showed statistically significant differences in relation to age group, nationality and work department with further statistically significant differences found in mean anxiety scores among nurses who had tested positive for COVID‐19 ( p  = 0.030; Table  2 ). Multivariate analysis revealed that the overall regression model was statistically significant ( F [3,620]  = 19.063, p  < 0.0001), with an R 2 of 29.1 (Table  S1 ). The R 2 is the proportion of variability, which means approximately 29% of the change in DASS scores was explained by age group (25–30 years), being a Saudi national and working in emergency or “other” departments. The corresponding regression coefficients of these variables indicated that the DASS scores increased on average (i) by 6.334 units in nurses aged 20–30 years when compared to those aged 46–50 years, (ii) by 17.725 units in Saudi nationals when compared to South African nationals and (iii) by 11.699 units in nurses who worked in emergency departments when compared to those who worked in outpatient departments (Table  S1 ).

For ENSS scores, bivariate analysis showed statistically significant differences related to nationality, place of work and experience (Table  3 ). Multivariate analysis showed that the overall regression model was statistically significant ( F [5,618]  = 19.754, p  < 0.0001) with an R 2 of 37.1 (Table  S2 ). A 37% change in ENSS score was explained by nationality and place of work. The corresponding regression coefficients of these variables indicated that ENSS scores increased, on average, (i) by 5.619 units in Filipino nationals when compared to Indian nationals, (ii) by 7.987 units in Malaysian nationals when compared to Indian nationals, (iii) by 4.976 units in Saudi nationals when compared to Indian nationals and (iv) by 4.996 units in nurses who worked in emergency departments when compared to those who worked in inpatient departments (Table  S2 ).

For JSS scores, bivariate analysis showed that the mean values had statistically significant differences in relation to nationality, place of work and education level (Table  4 ). Multivariate analysis showed that the overall regression model was statistically significant ( F [3,620]  = 19.063, p  < 0.0001), with an R 2 of 29 (Table  S3 ). A 29% change in JSS score was explained by nationality and place of work. The corresponding regression coefficients of these variables indicated that JSS scores increased, on average, (i) by 13.022 units in Indian nationals when compared with Filipino nationals, (ii) by 10.017 units in Saudi nationals when compared to Filipino nationals and (iii) by 9.992 units in nurses who worked in inpatient departments when compared to those who worked in outpatient departments (Table  S3 ).

5. DISCUSSION

The present study explored the impact of COVID‐19 on nurses working in a military hospital in Saudi Arabia and identified correlations between psychological symptoms and job satisfaction. The data give a detailed understanding of specific challenges to enable the study site to give additional support where needed, as well as give the wider field with new insights that can be built upon in future research. We found that the COVID‐19 pandemic is driving frontline nursing staff in the Jeddah region of Saudi Arabia to experience severe psychological strain.

Based on mean DASS scores, stress was the highest, when compared to depression and anxiety. This result is consistent with a meta‐analysis of 93 studies in which stress was found to be the most severe psychological symptom among nurses working during the COVID‐19 pandemic (Al Maqbali et al.,  2021 ). This result itself is unsurprising, as stress is considered a normal reaction to circumstances related to the pandemic, whereas depression and anxiety are considered psychiatric disorders that should meet certain symptom criteria for a specific duration (Regier et al.,  2013 ). However, nurses in the present study, who tested positive for COVID‐19 showed symptoms of anxiety. A previous qualitative exploration with nurses who had contracted COVID‐19 revealed similar results, while also providing further context regarding the depth of anxiety, fear and psychological shock they experienced (He et al.,  2021 ). However, as that was the only qualitative study, we were able to identify on this topic to date, we highlight this as an area that would benefit from further qualitative research not only to determine lived experiences but also to identify mitigating and supporting factors.

Data collected using the ENSS and JSS indicated that the most significant sources of stress for nursing staff in the present study were those associated with their work environment, such as workload, working under pressure, short time allotted to complete tasks, unsuitable rest/work regimens, frequent night shifts and overtime work. Pre‐pandemic, unusually high workloads were countered by reductions in outpatient appointments and treatments. However, the uniquely intense and demanding nature of COVID‐19 has made that an impossibility for isolation and triage hospitals. Similar findings have been reported elsewhere, as continuous emergency COVID‐19 cases, along with sustained increases in the number of suspected and confirmed cases, are placing frontline nursing staff under intense pressure (Brahmi et al.,  2020 ; Kakar et al.,  2021 ). Moreover, the extreme nature of COVID‐19 cases and high mortality rates have also changed the challenges nurses face in their work environment. New infection control safety policies have physically separated patients and families to reduce the risk of cross‐infection (Hsu et al.,  2020 ; Jaswaney et al.,  2022 ). Nurses implementing these policies have at times faced unreasonable demands and even abuse from distressed families, which exacerbates stressors and increases the pressure on them (Abu‐Snieneh,  2021 ). We found this to be the case among our nursing participants, who reported distress at the manner and frequency of patients deteriorating and dying, regardless of all medical and nursing efforts and care. These encounters led to a sense that the pandemic cannot be overcome, causing some nurses to experience guilt and self‐blame. This phenomenon has been noted elsewhere, as nurses have responded to blaming themselves, distressed, or angry relatives and patients and cited as one of the main stressors among frontline nurses (Byrne et al.,  2021 ; Liu et al.,  2020 ). We suggest that training in end‐of‐life care processes and approaches may be beneficial to give nurses with the skills to care for patients and families and to equip them with resiliency skills for this type of care (Peters et al.,  2013 ).

Frontline nurses were further impacted by the department in which they worked. We found nurses who worked in emergency departments scored the highest on the DASS, and ENSS, which is consistent with another study showing that nurses working in high‐exposure units with suspected COVID‐19 patients had higher levels of depression than nurses working in other units (Doo et al.,  2021 ). There could be several reasons for this finding, such as an unsafe work environment, insufficient personal protective equipment and unknown patient conditions. In addition, emergency departments are known to be unpredictable work environments, which not only means nurses must be ready to respond to any potential patient need but also increases their vulnerability to unexpected events, such as workplace violence and crises (Cui et al.,  2021 ).

There were other multiple domains on the ENSS and JSS that contributed to frontline nurses experiencing occupational stress and lacking job satisfaction, respectively. Interestingly, one correlation that was found was between the level of satisfaction and the level of education. Other researchers have found that the higher the level of education, the higher the level of satisfaction (Coomber & Barriball,  2007 ). Conversely in the present study, we found that the higher the level of education, the lower the level of satisfaction. One possible explanation for this could be that during the COVID‐19 pandemic, nurses with higher levels of education are more prepared and equipped to understand evidence‐based practice and policies and guidelines, and the absence of such may have contributed towards feelings of distress and lower satisfaction than nurses who are less highly trained and may not be as aware of the lack of research underpinning rapidly developed new policies and guidelines. This finding is at odds with other studies exploring this relationship (Lorber & Skela Savič,  2012 ). Another possible reason is that “job satisfaction” has not been consistently defined across studies (Coomber & Barriball,  2007 ), and those previous studies were performed in other counties where the term's meaning may have different cultural nuances.

Another area of note was as a perceived lack of support from supervisors. Although they are generally more experienced than their subordinates, nursing supervisors have been asked to serve in their roles with greater demands on them to manage an unfamiliar scenario (Alnazly et al.,  2021 ). As such, previously developed regulations, protocols and processes have not been effective or appropriate for responding to changing patient needs or care practices for infection control management; thus, supervisors have simply not had the information needed to guide practice and support junior staff, patients and families (Buheji & Buhaid,  2020 ). We found the nature of relationships to be a consistent source of stress for nurses, with conflicts between co‐workers (nurse to nurse) and with physicians, and a sense of continuous blame directed at nurses being particularly challenging. This is not an unsubstantiated perception, as Wang et al. ( 2020 ) found that other medical professionals often treat nurses as scapegoats.

Age was of particular significance in the present study, as depression, anxiety and stress were significantly higher in nurses aged 25–30 years. This is in line with the results of other studies with nurses in Saudi Arabia (Abu‐Snieneh,  2021 ; Ghawadra et al.,  2019 ) and internationally. For example, in China, Portugal and Turkey, younger frontline nurses were found to be more likely to experience depression and worry about personal or family health during the COVID‐19 pandemic (Murat et al.,  2021 ; Sampaio et al.,  2021 ; Zheng et al.,  2021 ). Potential explanations include a lack of preparedness for the occupational role in a pandemic and less experience responding to crisis situations among younger nurses, compared with older nurses (Shahrour & Dardas,  2020 ). Within our setting, another possible explanation connects to a prevailing cultural expectation. In Arab cultures it is expected that by age 25, most people will have settled down and established a family. Thus, attempts to meet expectations, such as finding the right partner, during the pandemic while experiencing mental and physical distress is likely to increase the negative psychological impact on individuals in this age group.

Nationality was of particular interest, as although the five nationalities of nurses captured in the questionnaire (Filipino, Indian, Malaysian, Saudi and South African) were not normally distributed, Saudi nurses showed higher levels of depression, anxiety and stress than nurses of other nationalities. Similar findings were reported by Al‐Dossary et al. ( 2020 ), whose study on the effect of COVID‐19 in 500 nurses found that non‐Saudi nurses had higher self‐reported awareness, positive attitudes, optimal prevention and positive perceptions compared with Saudi nurses. A possible explanation is that many non‐Saudi nurses working in the region are away from their families, while Saudi nurses are in their usual living arrangements. Therefore, during the pandemic, Saudi nurses have an additional concern of transmitting the virus to their families, while non‐Saudi nationals may be concerned about their loved ones, but do not experience the distress of their job leading to direct risk or harm to them (Abu‐Snieneh,  2021 ). Other studies have also shown family safety to be a significant concern among frontline nursing staff during the COVID‐19 pandemic (Labrague,  2021 ).

5.1. Limitations

The present study has some limitations that should be noted. Although this study provides insights into the main psychological stressors that are impacting the nursing workforce and to what degree, it would have been strengthened by including a qualitative arm to provide context and depth to our findings. This research is planned as our next phase. Survey tools were delivered in their original English language as our hospital nursing staff includes a wide range of nationalities and English is the official language of Saudi healthcare organizations. However, it may be beneficial in future research to develop alternative translations and variables that would more directly capture cultural context.

6. CONCLUSION

The present findings demonstrated a relationship between stress, psychological symptoms and job satisfaction. The main concerns were workload, work department, supervision, collegial relationships and high mortality rates in patients. More research is needed to identify what types of support are required, along with mechanisms to tailor such support to the different variables identified by the nursing participants. Based on the findings of this study, we recommend focusing efforts on raising awareness among hospital managers regarding nurses' psychological symptoms and possible support measures, which may include flexible working hours, clear communication and training in palliative and end‐of‐life care. Finally, qualitative investigation is highly recommended to explore in‐depth further context for the identified sources of stress, and psychological and emotional experiences among nurses as frontline workers facing COVID‐19. A co‐design approach may be particularly beneficial, as this will not only lead to strategies that draw from the knowledge and experience of the nursing staff but also potentially offer these nurses the opportunity to take back some control in a time of immense instability.

AUTHOR CONTRIBUTIONS

All authors listed have met all four of the following criteria: Have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; Been involved in drafting the manuscript or revising it critically for important intellectual content; Given final approval of the version to be published. Agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

FUNDING INFORMATION

This research received no specific grant from any funding agency in the public, commercial or not‐for‐profit sectors.

CONFLICT OF INTEREST

The authors have no conflict of interest to declare.

ETHICS STATEMENT

Ethical approval was obtained from the King Fahd Armed Forces Hospital—Jeddah Research and Ethics Committee (Ref. number: REC 398), confirming no risk to study participants via the application of an anonymous online survey. This study conforms to the recognized standards listed by the Declaration of Helsinki.

Supporting information

Sharif, L. , Almutairi, K. , Sharif, K. , Mahsoon, A. , Banakhar, M. , Albeladi, S. , Alqahtani, Y. , Attar, Z. , Abdali, F. , & Wright, R. (2023). Quantitative research on the impact of COVID‐19 on frontline nursing staff at a military hospital in Saudi Arabia . Nursing Open , 10 , 217–229. 10.1002/nop2.1297 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

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A visualized and bibliometric analysis of nursing research during the COVID-19 pandemic

Affiliations.

• 1 Department of Cardiology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
• 2 Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
• PMID: 39121302
• PMCID: PMC11315485
• DOI: 10.1097/MD.0000000000039245

Background: The global spread of Coronavirus disease 2019 (COVID-19) has been increasing since December 2019. A total of 8460 publications were obtained from the Web of Science Core Collection from 2019 to 2023, providing insights into the progress of nursing research throughout the COVID-19 pandemic.

Methods: Bibliometric analysis was conducted on these articles using CiteSpace. The analysis focused on examining the distribution of these publications in terms of space and time, distribution of authors, subject categories, distribution of topics, and cited references.

Results: These results may be explained from 3 perspectives. Initially, the number of yearly publications on nursing research consistently increased during the COVID-19 pandemic. Furthermore, a co-occurrence analysis of the countries and the authors revealed that certain countries, including the United States, China, and England, have successfully implemented organized and standardized nursing models. These countries also have well-developed and established nursing research systems. Notably, academic communities in specific regions, such as the team led by MD Stefan Gravenstein, Mor Vincent, and White Elizabeth at Brown University in the United States, have emerged as leaders in this field. Furthermore, examining the papers' subject categories and topic distribution indicate that nursing during the COVID-19 pandemic has been predominantly interdisciplinary, encompassing various disciplines such as clinical medicine, essential medicine, psychology, public health management, and even telematics science.

Conclusion subsectiongs: Our study provided valuable insights into acquiring knowledge on nursing research during the COVID-19 pandemic, pinpointed possible partners for researchers interested in nursing, and uncovered prevalent research patterns and popular subjects.

Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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Conflict of interest statement

The authors have no conflicts of interest to disclose.

Yearly quantity and document type…

Yearly quantity and document type of publications about nursing research during the COVID-19…

Network of countries of publications…

Network of countries of publications about nursing research during the COVID-19 pandemic.

Network of authors of publications…

Network of authors of publications about nursing research during the COVID-19 pandemic.

Network of institutions of publications…

Network of institutions of publications about nursing research during the COVID-19 pandemic. UDICE,…

Keyword co-occurrence and clustering. (A)…

Keyword co-occurrence and clustering. (A) Network of the main keywords in publications about…

Co-citation network and timeline view…

Co-citation network and timeline view of references cited by publications about nursing research…

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Interprofessional Synergy: Transforming Radiology with MR Fingerprinting

August 15, 2024

By Daniel Simon, MD , Chaitra Badve, MD , and Dan Ma, PhD

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Daniel Simon, MD: Hello everyone. Thank you for listening to another episode of Science@UH I am your host Dr. Dan Simon and today I am happy to be joined by Dr. Chaitra Badve and Dr. Dan Ma.

Dr. Badve is the Director of Magnetic Resonance Imaging at UH Cleveland Medical Center and Associate Professor at Case Western Reserve School of Medicine. Dr. Ma is an Associate Professor in the Department of Biomedical Engineering and Radiology at Case Western. They are the part of the radiology team who are at the forefront of the revolution of magnetic resonance fingerprinting imaging. Welcome Dr. Badve and Dr. Ma.

Chaitra Badve, MD:   Thank you for having us.

Dan Ma, PhD: Thank you, Dan Simon, for having us.

Daniel Simon, MD: Great. Well, it's great to be joined by both of you today. And I think before we begin, I want to take one little step back for some of our listeners who may not be familiar with principles of imaging, both CT based and MRI based. Maybe we could get really basic, to start out, and just ask you to tell us the difference between radiation based CT imaging and MRI imaging. What are we measuring? And what is the big advance of MR first over CT? Maybe Dr. Badve you could start with that.

Chaitra Badve, MD:   So CT scan as we know is a radiation based technology that uses X-rays that pass through a patient and based on the tissue density, how dense the tissue is, it is going to create an image which is density based. So, when you have a CT scan of head, you have certain Hounsfield units or density measures for each structure in the brain that will be a different measurement for CSF, for gray matter, for white matter, for skull, air and so on. And water is considered as Hounsfield units of zero and all the other structures are based on comparative density to water. Magnetic resonance imaging, on the other hand, is a radiation free technique. It uses very strong magnetic fields that can stimulate the water molecules in human body or human tissue and based on the signal that is generated by those protons an image is generated.

So the strengths of MRI are; there is very superior soft tissue resolution and that allows us to take a detailed look at structures within the human body, which is not afforded by CT scan.

Daniel Simon, MD: Great. OK, so now here's the big question for both of you. I've had a lot of MRI scans in my lifetime, and I've always wanted to know what's all the banging about? Why is it so noisy? So, Dr. Ma, tell me, why is it so noisy?

Dan Ma, MD: So the MRI scan is a big magnet. The noisy sound is from the actual scan. So whenever there is a scan, you will hear the loud noise. Of course, there's some technology to reduce the noise, so typically we have, earplugs and headphones and there's also in terms of research, we also develop some technology. There's also other product available to reduce the sound, so the sound, it's from the scan itself. It's really hard to reduce the sound. That's why we generate image from those. But there are technologies to reduce the sound.

Daniel Simon, MD: Great. OK. So now we're gonna move into this new part of MR Imaging, which I guess you would say to a layperson like me, a cardiologist, you know, we're just plumbers. We're not as sophisticated as you, imagers. Is the ability now to do special things to these images, a combination I guess, of image analysis and AI and other things to learn a lot of new things about tissues, almost like virtual histology? Perhaps you could explain to us, Dr. Ma, you were on the first author of this Seminole paper in nature about MRI fingerprinting. What is it exactly? Why is it so powerful?

Dan Ma, PhD: To start with, MR Fingerprinting is a new scan, so it's a different acquisition and also involve different image analysis. So we generate different images as compared to clinical MR. The big difference is that the new images, we call it quantitative measurements versus most current MR scans, which provide qualitative measurement. What does that mean is that, so, imagine MRI as looking at a picture where you can see which areas are lighter or darker that would indicate possible lesions or disease but a bit like checking whether someone has a fever by feeling their forehead, so it's a feeling, or you can read it by yourself, but there's no exact number and MR fingerprinting on the other side is like using thermometer to get the exact temperature from that person. So now you have a number that indicate how bad of the fever is, so this number will give doctors a specific number or tissue measurements that can help to spot the disease earlier or understand the disease better.

Daniel Simon, MD: So as a cardiologist who looks at an MR of the brain, obviously, as you pointed out, there are areas that are bright in areas that are dark. And what you're telling us now is that MR fingerprinting is going to tell us new information. And maybe Dr. Badve, you could help us understand what is that information telling us? Is it telling us the difference between normal tissue and tissue that has a brain tumor? Is it telling us something about vascular structures? What does the MR fingerprint give us insights into?

Chaitra Badve, MD:   So MRI fingerprinting is best defined as a framework. So you can change the sequence to make it sensitive to different tissue properties, T1 and T2 relaxation times, which are the fundamental units of MRI's, are the most basic things that we are measuring. But you can make it sensitive to measure other things like that…products are slow. Based on MRI and MR fingerprinting information, we are able to define ranges for the first time for normal and abnormal. So, as you said, you can differentiate normal tissue from abnormal tissue, but even in abnormalities you can differentiate or characterize the lesions further and classify them. So for example, in our work we are showing that you can differentiate between grades of tumor, you can differentiate between IDH mutant and wild type tumor. So, it gives you information that is above and beyond routine MRI and most of this information is actually actionable, which can contribute to patient’s direct care.

Daniel Simon, MD: You're actually telling us, in essence, that an MR fingerprint is almost like virtual histology. You can look at an image, you're not really biopsying the tissue, but you can actually tell the characteristics of what kind of tumor this is. Is it gonna be aggressive? Is it responding to treatment? Is that what you're saying?

Chaitra Badve, MD: Yes, exactly. MR fingerprinting is, for the first time giving us that information. You have the ability to see some of this on MRI, but as Dan mentioned, Dr. Ma, that this information is very qualitative and it depends on the reader skills, the scan quality and all of that. So. It's usually defined as hypointense or hyperintense. And now we are moving away from subjective interpretation to being more objective and data-driven in making these calls.

Dan Ma, PhD: I would like to add two things from what Dr. Badve has mentioned. So, I see another benefit of having those numbers is that you can now detect some mild subtle lesions now and it can improve the sensitivity of detecting those disease earlier.

Let's take this fever for example. It is really hard to tell, low fever, of course, if their forehead is super-hot, then you can say they’ve got a bad fever, but what if they only have a, you know, mild fever? They may feel sick, but not necessarily high fever. So in this range, I think giving a number is helpful.

So, for example, we've applied MR fingerprinting to detect very subtle lesions from epilepsy patients where typical MRI cannot detect. So this is one example and another factor, I think, having number is useful is if we look at longitudinally. So we want to track the change.

So for example, we have an application of using MR fingerprinting for pediatric patients. We will want to measure, what's their developmental delay? So we measure multiple points along their development and using those numbers, we can track their developmental trajectories and predict whether they have developmental delay or not. So, having those number or measuring the difference can really help us on this rather than saying whether their image is brighter or darker.

Daniel Simon, MD: Thank you very much. That's really helpful. So congratulations to both of you on your recently awarded $3.5 million NIH grant to study MR fingerprinting and analysis platform for brain tumors. This grant which you both serve as the principal investigators is a great demonstration of interprofessional team science collaboration between basic and clinical scientists. Can you tell us more about the project? What are you hoping to accomplish?

Dan Ma, PhD: So, this is a multiple site academic industry partnership grant that involves Case Western Reserve University, University Hospitals, University of Pennsylvania and the Siemens Healthineers. So this collaboration aims to integrate advanced imaging technology such as MR fingerprinting and AI based image analysis into everyday clinical workflow. So ultimately we hope to benefit patients worldwide.

So from the technical side, one of our goal is to enhance the speed and accuracy of brain tumor diagnosis through new MR Scan and AI based prediction method. So those technology, what we want to be easily adaptable in the clinical setting. So we are tackling technology challenges to ensure that this innovations, this technology, is not only meet the high standard for different patient needs, but also seamlessly fit in within very busy schedule of the clinical environment.

Another key focus from the technical side is transferring those new technology from a research tool into a practical clinical product. So that involves developing some infrastructure in collaboration with University Hospitals and Siemens Healthineers. So this involves translating MR fingerprinting scan from a research tool to an FDA approved product so that the scan can be installed in any MR scanners and it can be done as a, press the button, easy to operate.

So radiologists will be able to read those MR fingerprinting images like other clinical images without waiting for research process. This also applies to image analysis and disease predictions, which will also be available in the clinical setting, so radiologists don't need to understand the technology background in order to, you know, use those tools and interpret those results. And the last point I want to mention that this grant is academic industry partnership. So Siemens will play a big role of integrating those technology into the clinical workflow and also help us to expand this technology to a bigger impact. So let's say expanding this distribution of this technology worldwide. This is through their global digital market. They will make this technology available for all the Siemens sites over 4000 institutions in 60 countries. So this grant will aim to improve this brain tumor diagnosis tool and treatment process and also ensure that this innovations can be accessible worldwide, to have a bigger impact.

Daniel Simon, MD: Dr. Badve, when I've gone for a scan, it takes 30 plus minutes. Tell our listeners how much additional scanning time is there for an MR fingerprint, or is it part of the routine scanning? Does it add double the time or is it only a few extra minutes?

Chaitra Badve, MD: It's a few extra minutes, in short, so as I was saying earlier, MR fingerprinting can measure multiple properties and the most commonly used can measure the T1, T2. A whole brain's 3D high resolution MR fingerprinting scan of brain requires about 5 minutes for acquisition. And since it's purely quantitative data, we can use it for not just data analysis on the quantitative maps that we are using for various research purposes, but also generate synthetic images. So all the images that are routine MRI scan has T1 weighted T2 weighted layer. We can actually generate them from the single acquisition, so it is very possible in the near future to have a 5 minute scan that the patient will have. It will give you quantitative information about T1-T2, myelin, brain volumetry and plus give us all the qualitative scans that the radiologists are used at looking at. So it has multiple advantages in terms of acquisition and processing.

Daniel Simon, MD: So we've talked a lot about MR fingerprinting in the brain, but I understand that it's also now being applied to breast, pancreas and prostate as well. Can you just let our listeners know about where else it's being used and what its application could end up being?

Chaitra Badve, MD:  MR fingerprinting is being applied outside the brain, the key areas that UH is working on are breast imaging, renal cancers as well as prostate cancers. And we have wonderful radiologists here at UH, who are leading these projects. All of these projects have been funded by independent RO-1’s with federal funding and the initial results are really stunning. So for example, in breast imaging the preliminary data shows that you can differentiate between responders and non-responders for new adjuvant chemotherapy, which is a very, very important clinical question to address.

In renal cell cancers, again the focus of clinical application is to differentiate between high grade and clinically significant neoplasms from neoplasms that are not as clinically significant and the same for prostate cancer. So actually in prostate we have made a lot of progress where we have looked at various aspects of prostate cancer and differentiating lasers that can be just surveilled with passive surveillance, with lesions that need active treatment and the people who are leading these projects are Dr. Marshall in breast imaging, Dr. Tirumani for renal cell cancers and Dr. Bittencourt for prostate.

Daniel Simon, MD: How are we integrating magnetic resonance fingerprinting into clinical care at UH right now? Is it being used to make clinical decisions or is it only research based tool?

Chaitra Badve, MD: It is both, and UH, it is the first institution across the world where we have integrated MR fingerprinting scan into the clinical protocol. Currently, this is available only for brain imaging, but very soon it will be expanded out to breast, prostate imaging as well.

For every patient that's coming into CMC, the standard MRI brain protocol now includes MR fingerprinting as a routine. And these images are rapidly reconstructed and immediately shared on the clinical path. So a radiologist has access to all the routine MRI images as well as fingerprinting maps. And we are implementing these in brain tumor patients, dementia patients, epilepsy, post op follow-ups and then just routine scans follow up strokes. So we are collecting a wealth of data to look at MR fingerprinting as a population research tool, but it is also giving us novel opportunities to do bedside or reading room research where you are looking for the first time at actually one and two values for different lesions in the brain which we were not able to measure before so it has clinical implications, but also has tremendous research applications in the near future.

Daniel Simon, MD: Great. So you know our listeners today need to know that we're talking to two superstars you're part of a group that has over $49 million in funding from NIH got another $9 million in grants in 2023 alone, and more in 2024. We haven't even counted it up yet. So you have this amazing team. I mean, stars from Jeff Dirk Sunshine, Mark Griswold, Galani all these superstars, what's your secret sauce? How do you guys, you biomedical engineers, clinical MRI people? What, how do you do this? What's the secret sauce?

Chaitra Badve, MD: I think the secret sauce is we focus on two things. One is no technology should be without a direct clinical translation. Most of the physicists, not most all of them are super focused on making their technology implementable and using it for betterment of patient care. And that makes it really helpful for a physician, like me, to work in a translational partnership with these scientists. The other thing is I think very strong focus on interpersonal relationships and collegiality and all the names that you mentioned, we are standing on their shoulders today and they have been our mentors and guides and we really appreciate the groundwork that they have laid for us, that we are reaping benefits from and I think the generation with us and the future generations will continue to do that going forward.

Daniel Simon, MD: Dr. Ma?

Dan Ma, PhD: I totally agree with what Dr. Badve has talked about. We really benefit from this very long term relationship between MD and PhD's and I want to emphasize that it takes a long time and effort to really build this environment. The first thing I can think of is the working environment, we work in the hospital, right next to the scanners. This allow us, for the PhD researchers to really feel this clinical setting. We see patient and we see what's the actual clinical workflow would be like and we scan patients. So this gives us a strong clinical needs. We know what the clinical importance or scientific importance is and we want to tackle that. This allow us to better understand the situation. And Dr. Badve’s office is right next to mine, similar to all other MD's office right next to a PhD's office. This allow us to have very consistent or continuous discussion about what's needed or what's the technical challenge talked about. We understand what's the technical challenge and I try to understand what's the clinical challenge and we talked almost every day about this. So this setting really enable us to work closely together to tackle what's important. And I also want to bring up that this team has a long standing collaboration with medical manufacturers, as well. So we have a long term over 30 year’s collaboration with Siemens which will allow us to quickly transfer any research technology to a clinical product. So we have patents, we have commercial product starting from decades ago.

For example, Dr. Grace was parallel imaging technology right now has become the routine MR scan built in every single scan really accelerate the scan two to three times as compared to the original one. We can see what research technology can be translated and for now, umm, our fingerprinting is also patented and licensed. Its FDA approved, so it will soon become a clinical product. Also distributed globally and become a product for other countries. So this is really academic industry and the hospital collaboration everything together.

Daniel Simon, MD: Well, how inspiring to be with two members of this special interprofessional team. I think you have the sense here of listening that the MD, PhD partnership is alive and thriving. It's so exciting to think that this MR Fingerprinting technology, which is going to spread around the world, started in the basement. So to speak, of University Hospital. Thank you so much for joining us today to learn more about research at University Hospitals. Please visit uhhospitals.org/UHresearch. Thank you so much, Dr. Badve and Dr. Ma.

Dan Ma, PhD: Thank you, Dr. Simon.

Chaitra Badve, MD: Thank you for having us. It was an honor.

Tags: Clinical Research , Research , Imaging , MRI , NIH Grant

Invesco Health Care Fund Q2 2024 Review

• Invesco Health Care fund outperformed its benchmark in Q2 2024.
• Health care equipment, life science tools & services and health care services were the most notable underperformers during the quarter.
• GLP-1 obesity drug headlines appear less of a headwind for companies that treat obesity-related diseases.
• We still have a neutral outlook for the health care sector but have continued to see signs ofimproving growth trends.

Jonathan Kitchen

Key takeaways

• Strong stock selection in pharmaceuticals, health care equipment and biotechnology contributed the most to relative performance during the quarter, along with an underweight in health care services.
• Health care lagged technology and communication services but outperformed materials and industrials. Health care equipment, life science tools & services and health care services were the most notable underperformers during the quarter.
• Health care utilization levels and procedure activity have been higher and the biopharma research funding environment has been improving. GLP-1 obesity drug headlines appear less of a headwind for companies that treat obesity-related diseases.

Manager perspective and outlook

The US equity market advanced during the second quarter as investors appeared to continue processing the possibility of a US Federal Reserve pivot to easier monetary policy in 2024.

The health care sector underperformed the overall US stock market during the second quarter. Health care lagged technology and communication services the most while outperforming materials and industrials.

We still have a neutral outlook for the health care sector but have continued to see signs of improving growth trends. We have been observing higher levels of health care utilization and procedure activity, which are a tailwind for health care equipment, distributors and facilities, but a headwind for managed care. We have seen an improving environment for biopharma research funding, which is positive for life science tools & services. Headlines about GLP-1 obesity drugs have continued to be positive for biopharma companies and now appear to be less negative for equipment and services companies that treat obesity-related diseases and diabetes.

The fund invests in premier health care companies that we believe are positioned to compound multi-year growth. We combine in-depth health care experience with bottom-up fundamental analysis to evaluate company management, identify growth prospects and manage risk.

Portfolio positioning

The fund’s largest absolute industry weights are in pharmaceuticals, biotechnology and health care equipment. At quarter end, the fund’s largest overweights relative to its benchmark were in health care facilities, distributors and biotechnology. The fund was underweight in pharmaceuticals, managed care, health care services and equipment. During the quarter, the fund’s positions in pharmaceuticals, biotechnology, health care supplies and facilities increased. Positions in distributors, managed care, health care equipment, health care technology and life science tools & services decreased.

Within biotechnology, we added Alnylam Pharmaceuticals ( ALNY ), Merus ( MRUS ) and ADMA Biologics ( ADMA ) and sold Legend Biotech ( LEGN ), Ionis Pharmaceuticals ( IONS ), Kyverna Therapeutics ( KYTX ) and Scholar Rock ( SRRK ). In health care equipment, we added Insulet ( PODD ) and sold Shockwave Medical (SWAV), Steris ( STE ) and Treace Medical Concepts ( TMCI ). In life science tools & services we added Bio-Techne ( TECH ) and sold West Pharmaceutical Services ( WST ). In managed care, we sold Molina Healthcare ( MOH ), Progyny ( PGNY ) and Humana ( HUM ). In distributors, we sold Cardinal Health ( CAH ). In facilities, we added Select Medical ( SEM ) and Brookdale Senior Living ( BKD ) and sold Surgery Partners ( SGRY ). We added ConvaTec ( OTCPK:CNVVF ) in supplies and sold Veeva ( VEEV ) and Evolent Health ( EVH ) in health care technology.

Second quarter 2024 additions to the fund:

Alnylam Pharmaceuticals makes Ribonucleic Acid ( RNA ) interference therapeutics to treat rare cardio-metabolic, infectious, central nervous system and ocular diseases.

Merus is an early-stage biotechnology company that makes antibody therapeutics to treat head and neck cancer.

ADMA Biologics makes immuno-technology plasma-derived therapeutics to treat infectious diseases and manage immune compromised patients.

Insulet is a medical device company that makes insulin pumps to treat diabetes.

Bio-Techne provides reagents, instruments, manufacturing services and testing services for the biopharma, academic and diagnostics end markets.

Select Medical operates hospitals that provide recovery, inpatient rehabilitation and outpatient rehabilitation for critical illness and injury.

Brookdale Senior Living provides assisted living, independent living, memory care, skilled nursing, continuing care retirement communities and at home care.

Convatec supplies medical products used to manage chronic conditions, such as advanced wound care, ostomy care, continence care and infusion care.

Top issuers (% of total net assets)

Top countries (% of total net assets)

Top industries (% of total net assets)

Performance highlights

Strong stock selection in pharmaceuticals, health care equipment and biotechnology added most to relative performance during the quarter. An underweight in health care services was also advantageous. These positive results were partially offset by stock selection in managed care and life science tools & services.

Contributors to performance

Eli Lilly is a large-cap pharmaceutical company that makes drugs to treat Alzheimer’s, cancer, diabetes, obesity, pain and autoimmune diseases. The company posted better-than-expected earnings and raised 2024 guidance due to continued success and expansion in its obesity treatments.

Boston Scientific makes surgical devices and medical equipment to treat cardiovascular, gastrointestinal and pulmonological conditions. The company reported better-than-expected organic growth driven by strength in cardiovascular, endoscopy and urology devices. The company also reported a strong launch for its pulsed field ablation system, Farapulse.

AstraZeneca is a large-cap pharmaceutical company that makes drugs to treat oncology, cardiovascular, renal, metabolism and respiratory diseases. The company delivered better-than-expected revenue and earnings, driven by success across its product categories.

Detractors from performance

DexCom makes wearable continuous glucose monitoring (CGM) devices used to monitor and treat diabetes. The company delivered strong organic growth, added new patients, had better-than-expected profit margins and provided higher revenue guidance. However, investor sentiment appeared to slip due to a combination of inventory destocking, health care plan switching and disruption of customer Medicaid claims due to a cyber-attack at a vendor.

Molina Healthcare provides managed health care services under the Medicaid and Medicare programs and through the state insurance Marketplace. The stock declined during the quarter, hampered by apparent concerns about higher-than-expected Medicaid claims.

Bruker makes instruments, microscopes and imaging technology used in biopharma research, development and diagnostics. The stock declined during the quarter amid lower- than-expected organic growth in the company’s mass spectrometry CALID segment due to shipment timing and tough earnings comparisons to prior quarters.

Top contributors (%)

Top detractors (%)

Standardized performance (%) as of June 30, 2024

Original Post

Editor's Note: The summary bullets for this article were chosen by Seeking Alpha editors.

Editor's Note: This article discusses one or more securities that do not trade on a major U.S. exchange. Please be aware of the risks associated with these stocks.

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