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Significance of the Study – Examples and Writing Guide

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Significance of the Study

Definition:

Significance of the study in research refers to the potential importance, relevance, or impact of the research findings. It outlines how the research contributes to the existing body of knowledge, what gaps it fills, or what new understanding it brings to a particular field of study.

In general, the significance of a study can be assessed based on several factors, including:

• Originality : The extent to which the study advances existing knowledge or introduces new ideas and perspectives.
• Practical relevance: The potential implications of the study for real-world situations, such as improving policy or practice.
• Theoretical contribution: The extent to which the study provides new insights or perspectives on theoretical concepts or frameworks.
• Methodological rigor : The extent to which the study employs appropriate and robust methods and techniques to generate reliable and valid data.
• Social or cultural impact : The potential impact of the study on society, culture, or public perception of a particular issue.

Types of Significance of the Study

The significance of the Study can be divided into the following types:

Theoretical Significance

Theoretical significance refers to the contribution that a study makes to the existing body of theories in a specific field. This could be by confirming, refuting, or adding nuance to a currently accepted theory, or by proposing an entirely new theory.

Practical Significance

Practical significance refers to the direct applicability and usefulness of the research findings in real-world contexts. Studies with practical significance often address real-life problems and offer potential solutions or strategies. For example, a study in the field of public health might identify a new intervention that significantly reduces the spread of a certain disease.

Significance for Future Research

This pertains to the potential of a study to inspire further research. A study might open up new areas of investigation, provide new research methodologies, or propose new hypotheses that need to be tested.

How to Write Significance of the Study

Here’s a guide to writing an effective “Significance of the Study” section in research paper, thesis, or dissertation:

• Background : Begin by giving some context about your study. This could include a brief introduction to your subject area, the current state of research in the field, and the specific problem or question your study addresses.
• Identify the Gap : Demonstrate that there’s a gap in the existing literature or knowledge that needs to be filled, which is where your study comes in. The gap could be a lack of research on a particular topic, differing results in existing studies, or a new problem that has arisen and hasn’t yet been studied.
• State the Purpose of Your Study : Clearly state the main objective of your research. You may want to state the purpose as a solution to the problem or gap you’ve previously identified.
• Contributes to the existing body of knowledge.
• Addresses a significant research gap.
• Offers a new or better solution to a problem.
• Impacts policy or practice.
• Leads to improvements in a particular field or sector.
• Identify Beneficiaries : Identify who will benefit from your study. This could include other researchers, practitioners in your field, policy-makers, communities, businesses, or others. Explain how your findings could be used and by whom.
• Future Implications : Discuss the implications of your study for future research. This could involve questions that are left open, new questions that have been raised, or potential future methodologies suggested by your study.

Significance of the Study in Research Paper

The Significance of the Study in a research paper refers to the importance or relevance of the research topic being investigated. It answers the question “Why is this research important?” and highlights the potential contributions and impacts of the study.

The significance of the study can be presented in the introduction or background section of a research paper. It typically includes the following components:

• Importance of the research problem: This describes why the research problem is worth investigating and how it relates to existing knowledge and theories.
• Potential benefits and implications: This explains the potential contributions and impacts of the research on theory, practice, policy, or society.
• Originality and novelty: This highlights how the research adds new insights, approaches, or methods to the existing body of knowledge.
• Scope and limitations: This outlines the boundaries and constraints of the research and clarifies what the study will and will not address.

Suppose a researcher is conducting a study on the “Effects of social media use on the mental health of adolescents”.

The significance of the study may be:

“The present study is significant because it addresses a pressing public health issue of the negative impact of social media use on adolescent mental health. Given the widespread use of social media among this age group, understanding the effects of social media on mental health is critical for developing effective prevention and intervention strategies. This study will contribute to the existing literature by examining the moderating factors that may affect the relationship between social media use and mental health outcomes. It will also shed light on the potential benefits and risks of social media use for adolescents and inform the development of evidence-based guidelines for promoting healthy social media use among this population. The limitations of this study include the use of self-reported measures and the cross-sectional design, which precludes causal inference.”

Significance of the Study In Thesis

The significance of the study in a thesis refers to the importance or relevance of the research topic and the potential impact of the study on the field of study or society as a whole. It explains why the research is worth doing and what contribution it will make to existing knowledge.

For example, the significance of a thesis on “Artificial Intelligence in Healthcare” could be:

• With the increasing availability of healthcare data and the development of advanced machine learning algorithms, AI has the potential to revolutionize the healthcare industry by improving diagnosis, treatment, and patient outcomes. Therefore, this thesis can contribute to the understanding of how AI can be applied in healthcare and how it can benefit patients and healthcare providers.
• AI in healthcare also raises ethical and social issues, such as privacy concerns, bias in algorithms, and the impact on healthcare jobs. By exploring these issues in the thesis, it can provide insights into the potential risks and benefits of AI in healthcare and inform policy decisions.
• Finally, the thesis can also advance the field of computer science by developing new AI algorithms or techniques that can be applied to healthcare data, which can have broader applications in other industries or fields of research.

Significance of the Study in Research Proposal

The significance of a study in a research proposal refers to the importance or relevance of the research question, problem, or objective that the study aims to address. It explains why the research is valuable, relevant, and important to the academic or scientific community, policymakers, or society at large. A strong statement of significance can help to persuade the reviewers or funders of the research proposal that the study is worth funding and conducting.

Here is an example of a significance statement in a research proposal:

Title : The Effects of Gamification on Learning Programming: A Comparative Study

Significance Statement:

This proposed study aims to investigate the effects of gamification on learning programming. With the increasing demand for computer science professionals, programming has become a fundamental skill in the computer field. However, learning programming can be challenging, and students may struggle with motivation and engagement. Gamification has emerged as a promising approach to improve students’ engagement and motivation in learning, but its effects on programming education are not yet fully understood. This study is significant because it can provide valuable insights into the potential benefits of gamification in programming education and inform the development of effective teaching strategies to enhance students’ learning outcomes and interest in programming.

Examples of Significance of the Study

Here are some examples of the significance of a study that indicates how you can write this into your research paper according to your research topic:

Research on an Improved Water Filtration System : This study has the potential to impact millions of people living in water-scarce regions or those with limited access to clean water. A more efficient and affordable water filtration system can reduce water-borne diseases and improve the overall health of communities, enabling them to lead healthier, more productive lives.

Study on the Impact of Remote Work on Employee Productivity : Given the shift towards remote work due to recent events such as the COVID-19 pandemic, this study is of considerable significance. Findings could help organizations better structure their remote work policies and offer insights on how to maximize employee productivity, wellbeing, and job satisfaction.

Investigation into the Use of Solar Power in Developing Countries : With the world increasingly moving towards renewable energy, this study could provide important data on the feasibility and benefits of implementing solar power solutions in developing countries. This could potentially stimulate economic growth, reduce reliance on non-renewable resources, and contribute to global efforts to combat climate change.

Research on New Learning Strategies in Special Education : This study has the potential to greatly impact the field of special education. By understanding the effectiveness of new learning strategies, educators can improve their curriculum to provide better support for students with learning disabilities, fostering their academic growth and social development.

Examination of Mental Health Support in the Workplace : This study could highlight the impact of mental health initiatives on employee wellbeing and productivity. It could influence organizational policies across industries, promoting the implementation of mental health programs in the workplace, ultimately leading to healthier work environments.

Evaluation of a New Cancer Treatment Method : The significance of this study could be lifesaving. The research could lead to the development of more effective cancer treatments, increasing the survival rate and quality of life for patients worldwide.

When to Write Significance of the Study

The Significance of the Study section is an integral part of a research proposal or a thesis. This section is typically written after the introduction and the literature review. In the research process, the structure typically follows this order:

• Title – The name of your research.
• Abstract – A brief summary of the entire research.
• Introduction – A presentation of the problem your research aims to solve.
• Literature Review – A review of existing research on the topic to establish what is already known and where gaps exist.
• Significance of the Study – An explanation of why the research matters and its potential impact.

In the Significance of the Study section, you will discuss why your study is important, who it benefits, and how it adds to existing knowledge or practice in your field. This section is your opportunity to convince readers, and potentially funders or supervisors, that your research is valuable and worth undertaking.

Advantages of Significance of the Study

The Significance of the Study section in a research paper has multiple advantages:

• Establishes Relevance: This section helps to articulate the importance of your research to your field of study, as well as the wider society, by explicitly stating its relevance. This makes it easier for other researchers, funders, and policymakers to understand why your work is necessary and worth supporting.
• Guides the Research: Writing the significance can help you refine your research questions and objectives. This happens as you critically think about why your research is important and how it contributes to your field.
• Attracts Funding: If you are seeking funding or support for your research, having a well-written significance of the study section can be key. It helps to convince potential funders of the value of your work.
• Opens up Further Research: By stating the significance of the study, you’re also indicating what further research could be carried out in the future, based on your work. This helps to pave the way for future studies and demonstrates that your research is a valuable addition to the field.
• Provides Practical Applications: The significance of the study section often outlines how the research can be applied in real-world situations. This can be particularly important in applied sciences, where the practical implications of research are crucial.
• Enhances Understanding: This section can help readers understand how your study fits into the broader context of your field, adding value to the existing literature and contributing new knowledge or insights.

Limitations of Significance of the Study

The Significance of the Study section plays an essential role in any research. However, it is not without potential limitations. Here are some that you should be aware of:

• Subjectivity: The importance and implications of a study can be subjective and may vary from person to person. What one researcher considers significant might be seen as less critical by others. The assessment of significance often depends on personal judgement, biases, and perspectives.
• Predictability of Impact: While you can outline the potential implications of your research in the Significance of the Study section, the actual impact can be unpredictable. Research doesn’t always yield the expected results or have the predicted impact on the field or society.
• Difficulty in Measuring: The significance of a study is often qualitative and can be challenging to measure or quantify. You can explain how you think your research will contribute to your field or society, but measuring these outcomes can be complex.
• Possibility of Overstatement: Researchers may feel pressured to amplify the potential significance of their study to attract funding or interest. This can lead to overstating the potential benefits or implications, which can harm the credibility of the study if these results are not achieved.
• Overshadowing of Limitations: Sometimes, the significance of the study may overshadow the limitations of the research. It is important to balance the potential significance with a thorough discussion of the study’s limitations.
• Dependence on Successful Implementation: The significance of the study relies on the successful implementation of the research. If the research process has flaws or unexpected issues arise, the anticipated significance might not be realized.

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How To Write Significance of the Study (With Examples) 

Whether you’re writing a research paper or thesis, a portion called Significance of the Study ensures your readers understand the impact of your work. Learn how to effectively write this vital part of your research paper or thesis through our detailed steps, guidelines, and examples.

Related: How to Write a Concept Paper for Academic Research

Table of Contents

What is the significance of the study.

The Significance of the Study presents the importance of your research. It allows you to prove the study’s impact on your field of research, the new knowledge it contributes, and the people who will benefit from it.

Related: How To Write Scope and Delimitation of a Research Paper (With Examples)

Where Should I Put the Significance of the Study?

The Significance of the Study is part of the first chapter or the Introduction. It comes after the research’s rationale, problem statement, and hypothesis.

Related: How to Make Conceptual Framework (with Examples and Templates)

Why Should I Include the Significance of the Study?

The purpose of the Significance of the Study is to give you space to explain to your readers how exactly your research will be contributing to the literature of the field you are studying 1 . It’s where you explain why your research is worth conducting and its significance to the community, the people, and various institutions.

How To Write Significance of the Study: 5 Steps

Below are the steps and guidelines for writing your research’s Significance of the Study.

1. Use Your Research Problem as a Starting Point

Your problem statement can provide clues to your research study’s outcome and who will benefit from it 2 .

Ask yourself, “How will the answers to my research problem be beneficial?”. In this manner, you will know how valuable it is to conduct your study. 

Let’s say your research problem is “What is the level of effectiveness of the lemongrass (Cymbopogon citratus) in lowering the blood glucose level of Swiss mice (Mus musculus)?”

Discovering a positive correlation between the use of lemongrass and lower blood glucose level may lead to the following results:

• Increased public understanding of the plant’s medical properties;
• Higher appreciation of the importance of lemongrass  by the community;
• Adoption of lemongrass tea as a cheap, readily available, and natural remedy to lower their blood glucose level.

Once you’ve zeroed in on the general benefits of your study, it’s time to break it down into specific beneficiaries.

2. State How Your Research Will Contribute to the Existing Literature in the Field

Think of the things that were not explored by previous studies. Then, write how your research tackles those unexplored areas. Through this, you can convince your readers that you are studying something new and adding value to the field.

3. Explain How Your Research Will Benefit Society

In this part, tell how your research will impact society. Think of how the results of your study will change something in your community. 

For example, in the study about using lemongrass tea to lower blood glucose levels, you may indicate that through your research, the community will realize the significance of lemongrass and other herbal plants. As a result, the community will be encouraged to promote the cultivation and use of medicinal plants.

4. Mention the Specific Persons or Institutions Who Will Benefit From Your Study

Using the same example above, you may indicate that this research’s results will benefit those seeking an alternative supplement to prevent high blood glucose levels.

5. Indicate How Your Study May Help Future Studies in the Field

You must also specifically indicate how your research will be part of the literature of your field and how it will benefit future researchers. In our example above, you may indicate that through the data and analysis your research will provide, future researchers may explore other capabilities of herbal plants in preventing different diseases.

Tips and Warnings

• Think ahead . By visualizing your study in its complete form, it will be easier for you to connect the dots and identify the beneficiaries of your research.
• Write concisely. Make it straightforward, clear, and easy to understand so that the readers will appreciate the benefits of your research. Avoid making it too long and wordy.
• Go from general to specific . Like an inverted pyramid, you start from above by discussing the general contribution of your study and become more specific as you go along. For instance, if your research is about the effect of remote learning setup on the mental health of college students of a specific university , you may start by discussing the benefits of the research to society, to the educational institution, to the learning facilitators, and finally, to the students.
• Seek help . For example, you may ask your research adviser for insights on how your research may contribute to the existing literature. If you ask the right questions, your research adviser can point you in the right direction.
• Revise, revise, revise. Be ready to apply necessary changes to your research on the fly. Unexpected things require adaptability, whether it’s the respondents or variables involved in your study. There’s always room for improvement, so never assume your work is done until you have reached the finish line.

Significance of the Study Examples

This section presents examples of the Significance of the Study using the steps and guidelines presented above.

Example 1: STEM-Related Research

Research Topic: Level of Effectiveness of the Lemongrass ( Cymbopogon citratus ) Tea in Lowering the Blood Glucose Level of Swiss Mice ( Mus musculus ).

Significance of the Study .

This research will provide new insights into the medicinal benefit of lemongrass ( Cymbopogon citratus ), specifically on its hypoglycemic ability.

Through this research, the community will further realize promoting medicinal plants, especially lemongrass, as a preventive measure against various diseases. People and medical institutions may also consider lemongrass tea as an alternative supplement against hyperglycemia. 

Moreover, the analysis presented in this study will convey valuable information for future research exploring the medicinal benefits of lemongrass and other medicinal plants.  

Example 2: Business and Management-Related Research

Research Topic: A Comparative Analysis of Traditional and Social Media Marketing of Small Clothing Enterprises.

Significance of the Study:

By comparing the two marketing strategies presented by this research, there will be an expansion on the current understanding of the firms on these marketing strategies in terms of cost, acceptability, and sustainability. This study presents these marketing strategies for small clothing enterprises, giving them insights into which method is more appropriate and valuable for them. 

Specifically, this research will benefit start-up clothing enterprises in deciding which marketing strategy they should employ. Long-time clothing enterprises may also consider the result of this research to review their current marketing strategy.

Furthermore, a detailed presentation on the comparison of the marketing strategies involved in this research may serve as a tool for further studies to innovate the current method employed in the clothing Industry.

Example 3: Social Science -Related Research.

Research Topic:  Divide Et Impera : An Overview of How the Divide-and-Conquer Strategy Prevailed on Philippine Political History.

Significance of the Study :

Through the comprehensive exploration of this study on Philippine political history, the influence of the Divide et Impera, or political decentralization, on the political discernment across the history of the Philippines will be unraveled, emphasized, and scrutinized. Moreover, this research will elucidate how this principle prevailed until the current political theatre of the Philippines.

In this regard, this study will give awareness to society on how this principle might affect the current political context. Moreover, through the analysis made by this study, political entities and institutions will have a new approach to how to deal with this principle by learning about its influence in the past.

In addition, the overview presented in this research will push for new paradigms, which will be helpful for future discussion of the Divide et Impera principle and may lead to a more in-depth analysis.

Example 4: Humanities-Related Research

Research Topic: Effectiveness of Meditation on Reducing the Anxiety Levels of College Students.

Significance of the Study: 

This research will provide new perspectives in approaching anxiety issues of college students through meditation. 

Specifically, this research will benefit the following:

 Community – this study spreads awareness on recognizing anxiety as a mental health concern and how meditation can be a valuable approach to alleviating it.

Academic Institutions and Administrators – through this research, educational institutions and administrators may promote programs and advocacies regarding meditation to help students deal with their anxiety issues.

Mental health advocates – the result of this research will provide valuable information for the advocates to further their campaign on spreading awareness on dealing with various mental health issues, including anxiety, and how to stop stigmatizing those with mental health disorders.

Parents – this research may convince parents to consider programs involving meditation that may help the students deal with their anxiety issues.

Students will benefit directly from this research as its findings may encourage them to consider meditation to lower anxiety levels.

Future researchers – this study covers information involving meditation as an approach to reducing anxiety levels. Thus, the result of this study can be used for future discussions on the capabilities of meditation in alleviating other mental health concerns.

Frequently Asked Questions

1. what is the difference between the significance of the study and the rationale of the study.

Both aim to justify the conduct of the research. However, the Significance of the Study focuses on the specific benefits of your research in the field, society, and various people and institutions. On the other hand, the Rationale of the Study gives context on why the researcher initiated the conduct of the study.

Let’s take the research about the Effectiveness of Meditation in Reducing Anxiety Levels of College Students as an example. Suppose you are writing about the Significance of the Study. In that case, you must explain how your research will help society, the academic institution, and students deal with anxiety issues through meditation. Meanwhile, for the Rationale of the Study, you may state that due to the prevalence of anxiety attacks among college students, you’ve decided to make it the focal point of your research work.

2. What is the difference between Justification and the Significance of the Study?

In Justification, you express the logical reasoning behind the conduct of the study. On the other hand, the Significance of the Study aims to present to your readers the specific benefits your research will contribute to the field you are studying, community, people, and institutions.

Suppose again that your research is about the Effectiveness of Meditation in Reducing the Anxiety Levels of College Students. Suppose you are writing the Significance of the Study. In that case, you may state that your research will provide new insights and evidence regarding meditation’s ability to reduce college students’ anxiety levels. Meanwhile, you may note in the Justification that studies are saying how people used meditation in dealing with their mental health concerns. You may also indicate how meditation is a feasible approach to managing anxiety using the analysis presented by previous literature.

3. How should I start my research’s Significance of the Study section?

– This research will contribute… – The findings of this research… – This study aims to… – This study will provide… – Through the analysis presented in this study… – This study will benefit…

Moreover, you may start the Significance of the Study by elaborating on the contribution of your research in the field you are studying.

4. What is the difference between the Purpose of the Study and the Significance of the Study?

The Purpose of the Study focuses on why your research was conducted, while the Significance of the Study tells how the results of your research will benefit anyone.

Suppose your research is about the Effectiveness of Lemongrass Tea in Lowering the Blood Glucose Level of Swiss Mice . You may include in your Significance of the Study that the research results will provide new information and analysis on the medical ability of lemongrass to solve hyperglycemia. Meanwhile, you may include in your Purpose of the Study that your research wants to provide a cheaper and natural way to lower blood glucose levels since commercial supplements are expensive.

5. What is the Significance of the Study in Tagalog?

In Filipino research, the Significance of the Study is referred to as Kahalagahan ng Pag-aaral.

• Draft your Significance of the Study. Retrieved 18 April 2021, from http://dissertationedd.usc.edu/draft-your-significance-of-the-study.html
• Regoniel, P. (2015). Two Tips on How to Write the Significance of the Study. Retrieved 18 April 2021, from https://simplyeducate.me/2015/02/09/significance-of-the-study/

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Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

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What is the Significance of a Study? Examples and Guide

If you’re reading this post you’re probably wondering: what is the significance of a study?

No matter where you’re at with a piece of research, it is a good idea to think about the potential significance of your work. And sometimes you’ll have to explicitly write a statement of significance in your papers, it addition to it forming part of your thesis.

In this post I’ll cover what the significance of a study is, how to measure it, how to describe it with examples and add in some of my own experiences having now worked in research for over nine years.

If you’re reading this because you’re writing up your first paper, welcome! You may also like my how-to guide for all aspects of writing your first research paper .

Looking for guidance on writing the statement of significance for a paper or thesis? Click here to skip straight to that section.

What is the Significance of a Study?

For research papers, theses or dissertations it’s common to explicitly write a section describing the significance of the study. We’ll come onto what to include in that section in just a moment.

However the significance of a study can actually refer to several different things.

Working our way from the most technical to the broadest, depending on the context, the significance of a study may refer to:

• Within your study: Statistical significance. Can we trust the findings?
• Wider research field: Research significance. How does your study progress the field?
• Commercial / economic significance: Could there be business opportunities for your findings?
• Societal significance: What impact could your study have on the wider society.
• And probably other domain-specific significance!

We’ll shortly cover each of them in turn, including how they’re measured and some examples for each type of study significance.

But first, let’s touch on why you should consider the significance of your research at an early stage.

Why Care About the Significance of a Study?

No matter what is motivating you to carry out your research, it is sensible to think about the potential significance of your work. In the broadest sense this asks, how does the study contribute to the world?

After all, for many people research is only worth doing if it will result in some expected significance. For the vast majority of us our studies won’t be significant enough to reach the evening news, but most studies will help to enhance knowledge in a particular field and when research has at least some significance it makes for a far more fulfilling longterm pursuit.

Furthermore, a lot of us are carrying out research funded by the public. It therefore makes sense to keep an eye on what benefits the work could bring to the wider community.

Often in research you’ll come to a crossroads where you must decide which path of research to pursue. Thinking about the potential benefits of a strand of research can be useful for deciding how to spend your time, money and resources.

It’s worth noting though, that not all research activities have to work towards obvious significance. This is especially true while you’re a PhD student, where you’re figuring out what you enjoy and may simply be looking for an opportunity to learn a new skill.

However, if you’re trying to decide between two potential projects, it can be useful to weigh up the potential significance of each.

Let’s now dive into the different types of significance, starting with research significance.

Research Significance

What is the research significance of a study.

Unless someone specifies which type of significance they’re referring to, it is fair to assume that they want to know about the research significance of your study.

Research significance describes how your work has contributed to the field, how it could inform future studies and progress research.

Where should I write about my study’s significance in my thesis?

Typically you should write about your study’s significance in the Introduction and Conclusions sections of your thesis.

It’s important to mention it in the Introduction so that the relevance of your work and the potential impact and benefits it could have on the field are immediately apparent. Explaining why your work matters will help to engage readers (and examiners!) early on.

It’s also a good idea to detail the study’s significance in your Conclusions section. This adds weight to your findings and helps explain what your study contributes to the field.

On occasion you may also choose to include a brief description in your Abstract.

What is expected when submitting an article to a journal

It is common for journals to request a statement of significance, although this can sometimes be called other things such as:

• Impact statement
• Significance statement
• Advances in knowledge section

Here is one such example of what is expected:

Impact Statement:  An Impact Statement is required for all submissions.  Your impact statement will be evaluated by the Editor-in-Chief, Global Editors, and appropriate Associate Editor. For your manuscript to receive full review, the editors must be convinced that it is an important advance in for the field. The Impact Statement is not a restating of the abstract. It should address the following: Why is the work submitted important to the field? How does the work submitted advance the field? What new information does this work impart to the field? How does this new information impact the field? Experimental Biology and Medicine journal, author guidelines

Typically the impact statement will be shorter than the Abstract, around 150 words.

Defining the study’s significance is helpful not just for the impact statement (if the journal asks for one) but also for building a more compelling argument throughout your submission. For instance, usually you’ll start the Discussion section of a paper by highlighting the research significance of your work. You’ll also include a short description in your Abstract too.

How to describe the research significance of a study, with examples

Whether you’re writing a thesis or a journal article, the approach to writing about the significance of a study are broadly the same.

I’d therefore suggest using the questions above as a starting point to base your statements on.

• Why is the work submitted important to the field?
• How does the work submitted advance the field?
• What new information does this work impart to the field?
• How does this new information impact the field?

Answer those questions and you’ll have a much clearer idea of the research significance of your work.

When describing it, try to clearly state what is novel about your study’s contribution to the literature. Then go on to discuss what impact it could have on progressing the field along with recommendations for future work.

Potential sentence starters

If you’re not sure where to start, why not set a 10 minute timer and have a go at trying to finish a few of the following sentences. Not sure on what to put? Have a chat to your supervisor or lab mates and they may be able to suggest some ideas.

• This study is important to the field because…
• These findings advance the field by…
• Our results highlight the importance of…
• Our discoveries impact the field by…

Now you’ve had a go let’s have a look at some real life examples.

Statement of significance examples

A statement of significance / impact:

Impact Statement This review highlights the historical development of the concept of “ideal protein” that began in the 1950s and 1980s for poultry and swine diets, respectively, and the major conceptual deficiencies of the long-standing concept of “ideal protein” in animal nutrition based on recent advances in amino acid (AA) metabolism and functions. Nutritionists should move beyond the “ideal protein” concept to consider optimum ratios and amounts of all proteinogenic AAs in animal foods and, in the case of carnivores, also taurine. This will help formulate effective low-protein diets for livestock, poultry, and fish, while sustaining global animal production. Because they are not only species of agricultural importance, but also useful models to study the biology and diseases of humans as well as companion (e.g. dogs and cats), zoo, and extinct animals in the world, our work applies to a more general readership than the nutritionists and producers of farm animals. Wu G, Li P. The “ideal protein” concept is not ideal in animal nutrition.  Experimental Biology and Medicine . 2022;247(13):1191-1201. doi: 10.1177/15353702221082658

And the same type of section but this time called “Advances in knowledge”:

Advances in knowledge: According to the MY-RADs criteria, size measurements of focal lesions in MRI are now of relevance for response assessment in patients with monoclonal plasma cell disorders. Size changes of 1 or 2 mm are frequently observed due to uncertainty of the measurement only, while the actual focal lesion has not undergone any biological change. Size changes of at least 6 mm or more in  T 1  weighted or  T 2  weighted short tau inversion recovery sequences occur in only 5% or less of cases when the focal lesion has not undergone any biological change. Wennmann M, Grözinger M, Weru V, et al. Test-retest, inter- and intra-rater reproducibility of size measurements of focal bone marrow lesions in MRI in patients with multiple myeloma [published online ahead of print, 2023 Apr 12].  Br J Radiol . 2023;20220745. doi: 10.1259/bjr.20220745

Other examples of research significance

Moving beyond the formal statement of significance, here is how you can describe research significance more broadly within your paper.

Describing research impact in an Abstract of a paper:

Three-dimensional visualisation and quantification of the chondrocyte population within articular cartilage can be achieved across a field of view of several millimetres using laboratory-based micro-CT. The ability to map chondrocytes in 3D opens possibilities for research in fields from skeletal development through to medical device design and treatment of cartilage degeneration. Conclusions section of the abstract in my first paper .

In the Discussion section of a paper:

We report for the utility of a standard laboratory micro-CT scanner to visualise and quantify features of the chondrocyte population within intact articular cartilage in 3D. This study represents a complimentary addition to the growing body of evidence supporting the non-destructive imaging of the constituents of articular cartilage. This offers researchers the opportunity to image chondrocyte distributions in 3D without specialised synchrotron equipment, enabling investigations such as chondrocyte morphology across grades of cartilage damage, 3D strain mapping techniques such as digital volume correlation to evaluate mechanical properties  in situ , and models for 3D finite element analysis  in silico  simulations. This enables an objective quantification of chondrocyte distribution and morphology in three dimensions allowing greater insight for investigations into studies of cartilage development, degeneration and repair. One such application of our method, is as a means to provide a 3D pattern in the cartilage which, when combined with digital volume correlation, could determine 3D strain gradient measurements enabling potential treatment and repair of cartilage degeneration. Moreover, the method proposed here will allow evaluation of cartilage implanted with tissue engineered scaffolds designed to promote chondral repair, providing valuable insight into the induced regenerative process. The Discussion section of the paper is laced with references to research significance.

How is longer term research significance measured?

Looking beyond writing impact statements within papers, sometimes you’ll want to quantify the long term research significance of your work. For instance when applying for jobs.

The most obvious measure of a study’s long term research significance is the number of citations it receives from future publications. The thinking is that a study which receives more citations will have had more research impact, and therefore significance , than a study which received less citations. Citations can give a broad indication of how useful the work is to other researchers but citations aren’t really a good measure of significance.

Bear in mind that us researchers can be lazy folks and sometimes are simply looking to cite the first paper which backs up one of our claims. You can find studies which receive a lot of citations simply for packaging up the obvious in a form which can be easily found and referenced, for instance by having a catchy or optimised title.

Likewise, research activity varies wildly between fields. Therefore a certain study may have had a big impact on a particular field but receive a modest number of citations, simply because not many other researchers are working in the field.

Nevertheless, citations are a standard measure of significance and for better or worse it remains impressive for someone to be the first author of a publication receiving lots of citations.

Other measures for the research significance of a study include:

• Accolades: best paper awards at conferences, thesis awards, “most downloaded” titles for articles, press coverage.
• How much follow-on research the study creates. For instance, part of my PhD involved a novel material initially developed by another PhD student in the lab. That PhD student’s research had unlocked lots of potential new studies and now lots of people in the group were using the same material and developing it for different applications. The initial study may not receive a high number of citations yet long term it generated a lot of research activity.

That covers research significance, but you’ll often want to consider other types of significance for your study and we’ll cover those next.

Statistical Significance

What is the statistical significance of a study.

Often as part of a study you’ll carry out statistical tests and then state the statistical significance of your findings: think p-values eg <0.05. It is useful to describe the outcome of these tests within your report or paper, to give a measure of statistical significance.

Effectively you are trying to show whether the performance of your innovation is actually better than a control or baseline and not just chance. Statistical significance deserves a whole other post so I won’t go into a huge amount of depth here.

Things that make publication in  The BMJ  impossible or unlikely Internal validity/robustness of the study • It had insufficient statistical power, making interpretation difficult; • Lack of statistical power; The British Medical Journal’s guide for authors

Calculating statistical significance isn’t always necessary (or valid) for a study, such as if you have a very small number of samples, but it is a very common requirement for scientific articles.

Writing a journal article? Check the journal’s guide for authors to see what they expect. Generally if you have approximately five or more samples or replicates it makes sense to start thinking about statistical tests. Speak to your supervisor and lab mates for advice, and look at other published articles in your field.

How is statistical significance measured?

Statistical significance is quantified using p-values . Depending on your study design you’ll choose different statistical tests to compute the p-value.

A p-value of 0.05 is a common threshold value. The 0.05 means that there is a 1/20 chance that the difference in performance you’re reporting is just down to random chance.

• p-values above 0.05 mean that the result isn’t statistically significant enough to be trusted: it is too likely that the effect you’re showing is just luck.
• p-values less than or equal to 0.05 mean that the result is statistically significant. In other words: unlikely to just be chance, which is usually considered a good outcome.

Low p-values (eg p = 0.001) mean that it is highly unlikely to be random chance (1/1000 in the case of p = 0.001), therefore more statistically significant.

It is important to clarify that, although low p-values mean that your findings are statistically significant, it doesn’t automatically mean that the result is scientifically important. More on that in the next section on research significance.

How to describe the statistical significance of your study, with examples

In the first paper from my PhD I ran some statistical tests to see if different staining techniques (basically dyes) increased how well you could see cells in cow tissue using micro-CT scanning (a 3D imaging technique).

In your methods section you should mention the statistical tests you conducted and then in the results you will have statements such as:

Between mediums for the two scan protocols C/N [contrast to noise ratio] was greater for EtOH than the PBS in both scanning methods (both  p  < 0.0001) with mean differences of 1.243 (95% CI [confidence interval] 0.709 to 1.778) for absorption contrast and 6.231 (95% CI 5.772 to 6.690) for propagation contrast. … Two repeat propagation scans were taken of samples from the PTA-stained groups. No difference in mean C/N was found with either medium: PBS had a mean difference of 0.058 ( p  = 0.852, 95% CI -0.560 to 0.676), EtOH had a mean difference of 1.183 ( p  = 0.112, 95% CI 0.281 to 2.648). From the Results section of my first paper, available here . Square brackets added for this post to aid clarity.

From this text the reader can infer from the first paragraph that there was a statistically significant difference in using EtOH compared to PBS (really small p-value of <0.0001). However, from the second paragraph, the difference between two repeat scans was statistically insignificant for both PBS (p = 0.852) and EtOH (p = 0.112).

By conducting these statistical tests you have then earned your right to make bold statements, such as these from the discussion section:

Propagation phase-contrast increases the contrast of individual chondrocytes [cartilage cells] compared to using absorption contrast. From the Discussion section from the same paper.

Without statistical tests you have no evidence that your results are not just down to random chance.

Beyond describing the statistical significance of a study in the main body text of your work, you can also show it in your figures.

In figures such as bar charts you’ll often see asterisks to represent statistical significance, and “n.s.” to show differences between groups which are not statistically significant. Here is one such figure, with some subplots, from the same paper:

In this example an asterisk (*) between two bars represents p < 0.05. Two asterisks (**) represents p < 0.001 and three asterisks (***) represents p < 0.0001. This should always be stated in the caption of your figure since the values that each asterisk refers to can vary.

Now that we know if a study is showing statistically and research significance, let’s zoom out a little and consider the potential for commercial significance.

Commercial and Industrial Significance

What are commercial and industrial significance.

Moving beyond significance in relation to academia, your research may also have commercial or economic significance.

Simply put:

• Commercial significance: could the research be commercialised as a product or service? Perhaps the underlying technology described in your study could be licensed to a company or you could even start your own business using it.
• Industrial significance: more widely than just providing a product which could be sold, does your research provide insights which may affect a whole industry? Such as: revealing insights or issues with current practices, performance gains you don’t want to commercialise (e.g. solar power efficiency), providing suggested frameworks or improvements which could be employed industry-wide.

I’ve grouped these two together because there can certainly be overlap. For instance, perhaps your new technology could be commercialised whilst providing wider improvements for the whole industry.

Commercial and industrial significance are not relevant to most studies, so only write about it if you and your supervisor can think of reasonable routes to your work having an impact in these ways.

How are commercial and industrial significance measured?

Unlike statistical and research significances, the measures of commercial and industrial significance can be much more broad.

Here are some potential measures of significance:

Commercial significance:

• How much value does your technology bring to potential customers or users?
• How big is the potential market and how much revenue could the product potentially generate?
• Is the intellectual property protectable? i.e. patentable, or if not could the novelty be protected with trade secrets: if so publish your method with caution!
• If commercialised, could the product bring employment to a geographical area?

Industrial significance:

What impact could it have on the industry? For instance if you’re revealing an issue with something, such as unintended negative consequences of a drug , what does that mean for the industry and the public? This could be:

• Reduced overhead costs
• Better safety
• Faster production methods
• Improved scaleability

How to describe the commercial and industrial significance of a study, with examples

Commercial significance.

If your technology could be commercially viable, and you’ve got an interest in commercialising it yourself, it is likely that you and your university may not want to immediately publish the study in a journal.

You’ll probably want to consider routes to exploiting the technology and your university may have a “technology transfer” team to help researchers navigate the various options.

However, if instead of publishing a paper you’re submitting a thesis or dissertation then it can be useful to highlight the commercial significance of your work. In this instance you could include statements of commercial significance such as:

The measurement technology described in this study provides state of the art performance and could enable the development of low cost devices for aerospace applications. An example of commercial significance I invented for this post

Industrial significance

First, think about the industrial sectors who could benefit from the developments described in your study.

For example if you’re working to improve battery efficiency it is easy to think of how it could lead to performance gains for certain industries, like personal electronics or electric vehicles. In these instances you can describe the industrial significance relatively easily, based off your findings.

For example:

By utilising abundant materials in the described battery fabrication process we provide a framework for battery manufacturers to reduce dependence on rare earth components. Again, an invented example

For other technologies there may well be industrial applications but they are less immediately obvious and applicable. In these scenarios the best you can do is to simply reframe your research significance statement in terms of potential commercial applications in a broad way.

As a reminder: not all studies should address industrial significance, so don’t try to invent applications just for the sake of it!

Societal Significance

What is the societal significance of a study.

The most broad category of significance is the societal impact which could stem from it.

If you’re working in an applied field it may be quite easy to see a route for your research to impact society. For others, the route to societal significance may be less immediate or clear.

Studies can help with big issues facing society such as:

• Medical applications : vaccines, surgical implants, drugs, improving patient safety. For instance this medical device and drug combination I worked on which has a very direct route to societal significance.
• Political significance : Your research may provide insights which could contribute towards potential changes in policy or better understanding of issues facing society.
• Public health : for instance COVID-19 transmission and related decisions.
• Climate change : mitigation such as more efficient solar panels and lower cost battery solutions, and studying required adaptation efforts and technologies. Also, better understanding around related societal issues, for instance this study on the effects of temperature on hate speech.

How is societal significance measured?

Societal significance at a high level can be quantified by the size of its potential societal effect. Just like a lab risk assessment, you can think of it in terms of probability (or how many people it could help) and impact magnitude.

Societal impact = How many people it could help x the magnitude of the impact

Think about how widely applicable the findings are: for instance does it affect only certain people? Then think about the potential size of the impact: what kind of difference could it make to those people?

Between these two metrics you can get a pretty good overview of the potential societal significance of your research study.

How to describe the societal significance of a study, with examples

Quite often the broad societal significance of your study is what you’re setting the scene for in your Introduction. In addition to describing the existing literature, it is common to for the study’s motivation to touch on its wider impact for society.

For those of us working in healthcare research it is usually pretty easy to see a path towards societal significance.

Our CLOUT model has state-of-the-art performance in mortality prediction, surpassing other competitive NN models and a logistic regression model … Our results show that the risk factors identified by the CLOUT model agree with physicians’ assessment, suggesting that CLOUT could be used in real-world clinicalsettings. Our results strongly support that CLOUT may be a useful tool to generate clinical prediction models, especially among hospitalized and critically ill patient populations. Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

In other domains the societal significance may either take longer or be more indirect, meaning that it can be more difficult to describe the societal impact.

Even so, here are some examples I’ve found from studies in non-healthcare domains:

We examined food waste as an initial investigation and test of this methodology, and there is clear potential for the examination of not only other policy texts related to food waste (e.g., liability protection, tax incentives, etc.; Broad Leib et al., 2020) but related to sustainable fishing (Worm et al., 2006) and energy use (Hawken, 2017). These other areas are of obvious relevance to climate change… AI-Based Text Analysis for Evaluating Food Waste Policies

The continued development of state-of-the art NLP tools tailored to climate policy will allow climate researchers and policy makers to extract meaningful information from this growing body of text, to monitor trends over time and administrative units, and to identify potential policy improvements. BERT Classification of Paris Agreement Climate Action Plans

Top Tips For Identifying & Writing About the Significance of Your Study

• Writing a thesis? Describe the significance of your study in the Introduction and the Conclusion .
• Submitting a paper? Read the journal’s guidelines. If you’re writing a statement of significance for a journal, make sure you read any guidance they give for what they’re expecting.
• Take a step back from your research and consider your study’s main contributions.
• Read previously published studies in your field . Use this for inspiration and ideas on how to describe the significance of your own study
• Discuss the study with your supervisor and potential co-authors or collaborators and brainstorm potential types of significance for it.

Now you’ve finished reading up on the significance of a study you may also like my how-to guide for all aspects of writing your first research paper .

Writing an academic journal paper

I hope that you’ve learned something useful from this article about the significance of a study. If you have any more research-related questions let me know, I’m here to help.

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How To Write a Significance Statement for Your Research

A significance statement is an essential part of a research paper. It explains the importance and relevance of the study to the academic community and the world at large. To write a compelling significance statement, identify the research problem, explain why it is significant, provide evidence of its importance, and highlight its potential impact on future research, policy, or practice. A well-crafted significance statement should effectively communicate the value of the research to readers and help them understand why it matters.

Updated on May 4, 2023

A significance statement is a clearly stated, non-technical paragraph that explains why your research matters. It’s central in making the public aware of and gaining support for your research.

Write it in jargon-free language that a reader from any field can understand. Well-crafted, easily readable significance statements can improve your chances for citation and impact and make it easier for readers outside your field to find and understand your work.

Read on for more details on what a significance statement is, how it can enhance the impact of your research, and, of course, how to write one.

What is a significance statement in research?

A significance statement answers the question: How will your research advance scientific knowledge and impact society at large (as well as specific populations)? 

You might also see it called a “Significance of the study” statement. Some professional organizations in the STEM sciences and social sciences now recommended that journals in their disciplines make such statements a standard feature of each published article. Funding agencies also consider “significance” a key criterion for their awards.

Read some examples of significance statements from the Proceedings of the National Academy of Sciences (PNAS) here .

Depending upon the specific journal or funding agency’s requirements, your statement may be around 100 words and answer these questions:

1. What’s the purpose of this research?

2. What are its key findings?

3. Why do they matter?

4. Who benefits from the research results?

Readers will want to know: “What is interesting or important about this research?” Keep asking yourself that question.

Where to place the significance statement in your manuscript

Most journals ask you to place the significance statement before or after the abstract, so check with each journal’s guide. 

This article is focused on the formal significance statement, even though you’ll naturally highlight your project’s significance elsewhere in your manuscript. (In the introduction, you’ll set out your research aims, and in the conclusion, you’ll explain the potential applications of your research and recommend areas for future research. You’re building an overall case for the value of your work.)

Developing the significance statement

The main steps in planning and developing your statement are to assess the gaps to which your study contributes, and then define your work’s implications and impact.

Identify what gaps your study fills and what it contributes

Your literature review was a big part of how you planned your study. To develop your research aims and objectives, you identified gaps or unanswered questions in the preceding research and designed your study to address them.

Go back to that lit review and look at those gaps again. Review your research proposal to refresh your memory. Ask:

• How have my research findings advanced knowledge or provided notable new insights?
• How has my research helped to prove (or disprove) a hypothesis or answer a research question?
• Why are those results important?

Consider your study’s potential impact at two levels: 

• What contribution does my research make to my field?
• How does it specifically contribute to knowledge; that is, who will benefit the most from it?

Define the implications and potential impact

As you make notes, keep the reasons in mind for why you are writing this statement. Whom will it impact, and why?

The first audience for your significance statement will be journal reviewers when you submit your article for publishing. Many journals require one for manuscript submissions. Study the author’s guide of your desired journal to see its criteria ( here’s an example ). Peer reviewers who can clearly understand the value of your research will be more likely to recommend publication. 

Second, when you apply for funding, your significance statement will help justify why your research deserves a grant from a funding agency . The U.S. National Institutes of Health (NIH), for example, wants to see that a project will “exert a sustained, powerful influence on the research field(s) involved.” Clear, simple language is always valuable because not all reviewers will be specialists in your field.

Third, this concise statement about your study’s importance can affect how potential readers engage with your work. Science journalists and interested readers can promote and spread your work, enhancing your reputation and influence. Help them understand your work.

You’re now ready to express the importance of your research clearly and concisely. Time to start writing.

How to write a significance statement: Key elements 

When drafting your statement, focus on both the content and writing style.

• In terms of content, emphasize the importance, timeliness, and relevance of your research results. 
• Write the statement in plain, clear language rather than scientific or technical jargon. Your audience will include not just your fellow scientists but also non-specialists like journalists, funding reviewers, and members of the public. 

Follow the process we outline below to build a solid, well-crafted, and informative statement. 

Get started

Some suggested opening lines to help you get started might be:

• The implications of this study are… 
• Building upon previous contributions, our study moves the field forward because…
• Our study furthers previous understanding about…

Alternatively, you may start with a statement about the phenomenon you’re studying, leading to the problem statement.

Include these components

Next, draft some sentences that include the following elements. A good example, which we’ll use here, is a significance statement by Rogers et al. (2022) published in the Journal of Climate .

1. Briefly situate your research study in its larger context . Start by introducing the topic, leading to a problem statement. Here’s an example:

‘Heatwaves pose a major threat to human health, ecosystems, and human systems.”

2. State the research problem.

“Simultaneous heatwaves affecting multiple regions can exacerbate such threats. For example, multiple food-producing regions simultaneously undergoing heat-related crop damage could drive global food shortages.”

3. Tell what your study does to address it.

“We assess recent changes in the occurrence of simultaneous large heatwaves.”

4. Provide brief but powerful evidence to support the claims your statement is making , Use quantifiable terms rather than vague ones (e.g., instead of “This phenomenon is happening now more than ever,” see below how Rogers et al. (2022) explained it). This evidence intensifies and illustrates the problem more vividly:

“Such simultaneous heatwaves are 7 times more likely now than 40 years ago. They are also hotter and affect a larger area. Their increasing occurrence is mainly driven by warming baseline temperatures due to global heating, but changes in weather patterns contribute to disproportionate increases over parts of Europe, the eastern United States, and Asia.

5. Relate your study’s impact to the broader context , starting with its general significance to society—then, when possible, move to the particular as you name specific applications of your research findings. (Our example lacks this second level of application.) 

“Better understanding the drivers of weather pattern changes is therefore important for understanding future concurrent heatwave characteristics and their impacts.”

Refine your English

Don’t understate or overstate your findings – just make clear what your study contributes. When you have all the elements in place, review your draft to simplify and polish your language. Even better, get an expert AJE edit . Be sure to use “plain” language rather than academic jargon.

• Avoid acronyms, scientific jargon, and technical terms 
• Use active verbs in your sentence structure rather than passive voice (e.g., instead of “It was found that...”, use “We found...”)
• Make sentence structures short, easy to understand – readable
• Try to address only one idea in each sentence and keep sentences within 25 words (15 words is even better)
• Eliminate nonessential words and phrases (“fluff” and wordiness)

Enhance your significance statement’s impact

Always take time to review your draft multiple times. Make sure that you:

• Keep your language focused
• Provide evidence to support your claims
• Relate the significance to the broader research context in your field

After revising your significance statement, request feedback from a reading mentor about how to make it even clearer. If you’re not a native English speaker, seek help from a native-English-speaking colleague or use an editing service like AJE to make sure your work is at a native level.

Understanding the significance of your study

Your readers may have much less interest than you do in the specific details of your research methods and measures. Many readers will scan your article to learn how your findings might apply to them and their own research. 

Different types of significance

Your findings may have different types of significance, relevant to different populations or fields of study for different reasons. You can emphasize your work’s statistical, clinical, or practical significance. Editors or reviewers in the social sciences might also evaluate your work’s social or political significance.

Statistical significance means that the results are unlikely to have occurred randomly. Instead, it implies a true cause-and-effect relationship.

Clinical significance means that your findings are applicable for treating patients and improving quality of life.

Practical significance is when your research outcomes are meaningful to society at large, in the “real world.” Practical significance is usually measured by the study’s  effect size . Similarly, evaluators may attribute social or political significance to research that addresses “real and immediate” social problems.

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What is the Significance of the Study?

• By DiscoverPhDs
• August 25, 2020

• what the significance of the study means,
• why it’s important to include in your research work,
• where you would include it in your paper, thesis or dissertation,
• how you write one
• and finally an example of a well written section about the significance of the study.

What does Significance of the Study mean?

The significance of the study is a written statement that explains why your research was needed. It’s a justification of the importance of your work and impact it has on your research field, it’s contribution to new knowledge and how others will benefit from it.

Why is the Significance of the Study important?

The significance of the study, also known as the rationale of the study, is important to convey to the reader why the research work was important. This may be an academic reviewer assessing your manuscript under peer-review, an examiner reading your PhD thesis, a funder reading your grant application or another research group reading your published journal paper. Your academic writing should make clear to the reader what the significance of the research that you performed was, the contribution you made and the benefits of it.

How do you write the Significance of the Study?

When writing this section, first think about where the gaps in knowledge are in your research field. What are the areas that are poorly understood with little or no previously published literature? Or what topics have others previously published on that still require further work. This is often referred to as the problem statement.

The introduction section within the significance of the study should include you writing the problem statement and explaining to the reader where the gap in literature is.

Then think about the significance of your research and thesis study from two perspectives: (1) what is the general contribution of your research on your field and (2) what specific contribution have you made to the knowledge and who does this benefit the most.

For example, the gap in knowledge may be that the benefits of dumbbell exercises for patients recovering from a broken arm are not fully understood. You may have performed a study investigating the impact of dumbbell training in patients with fractures versus those that did not perform dumbbell exercises and shown there to be a benefit in their use. The broad significance of the study would be the improvement in the understanding of effective physiotherapy methods. Your specific contribution has been to show a significant improvement in the rate of recovery in patients with broken arms when performing certain dumbbell exercise routines.

This statement should be no more than 500 words in length when written for a thesis. Within a research paper, the statement should be shorter and around 200 words at most.

Significance of the Study: An example

Building on the above hypothetical academic study, the following is an example of a full statement of the significance of the study for you to consider when writing your own. Keep in mind though that there’s no single way of writing the perfect significance statement and it may well depend on the subject area and the study content.

Here’s another example to help demonstrate how a significance of the study can also be applied to non-technical fields:

The significance of this research lies in its potential to inform clinical practices and patient counseling. By understanding the psychological outcomes associated with non-surgical facial aesthetics, practitioners can better guide their patients in making informed decisions about their treatment plans. Additionally, this study contributes to the body of academic knowledge by providing empirical evidence on the effects of these cosmetic procedures, which have been largely anecdotal up to this point.

The statement of the significance of the study is used by students and researchers in academic writing to convey the importance of the research performed; this section is written at the end of the introduction and should describe the specific contribution made and who it benefits.

Fieldwork can be essential for your PhD project. Use these tips to help maximise site productivity and reduce your research time by a few weeks.

The unit of analysis refers to the main parameter that you’re investigating in your research project or study.

There’s no doubt about it – writing can be difficult. Whether you’re writing the first sentence of a paper or a grant proposal, it’s easy

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• Knowledge Base

An Easy Introduction to Statistical Significance (With Examples)

Published on January 7, 2021 by Pritha Bhandari . Revised on June 22, 2023.

If a result is statistically significant , that means it’s unlikely to be explained solely by chance or random factors. In other words, a statistically significant result has a very low chance of occurring if there were no true effect in a research study.

The p value , or probability value, tells you the statistical significance of a finding. In most studies, a p value of 0.05 or less is considered statistically significant, but this threshold can also be set higher or lower.

Table of contents

How do you test for statistical significance, what is a significance level, problems with relying on statistical significance, other types of significance in research, other interesting articles, frequently asked questions about statistical significance.

In quantitative research , data are analyzed through null hypothesis significance testing, or hypothesis testing. This is a formal procedure for assessing whether a relationship between variables or a difference between groups is statistically significant.

Null and alternative hypotheses

To begin, research predictions are rephrased into two main hypotheses: the null and alternative hypothesis .

• A null hypothesis ( H 0 ) always predicts no true effect, no relationship between variables , or no difference between groups.
• An alternative hypothesis ( H a or H 1 ) states your main prediction of a true effect, a relationship between variables, or a difference between groups.

Hypothesis testin g always starts with the assumption that the null hypothesis is true. Using this procedure, you can assess the likelihood (probability) of obtaining your results under this assumption. Based on the outcome of the test, you can reject or retain the null hypothesis.

• H 0 : There is no difference in happiness between actively smiling and not smiling.
• H a : Actively smiling leads to more happiness than not smiling.

Test statistics and p values

Every statistical test produces:

• A test statistic that indicates how closely your data match the null hypothesis.
• A corresponding p value that tells you the probability of obtaining this result if the null hypothesis is true.

The p value determines statistical significance. An extremely low p value indicates high statistical significance, while a high p value means low or no statistical significance.

Next, you perform a t test to see whether actively smiling leads to more happiness. Using the difference in average happiness between the two groups, you calculate:

• a t value (the test statistic) that tells you how much the sample data differs from the null hypothesis,
• a p value showing the likelihood of finding this result if the null hypothesis is true.

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The significance level , or alpha (α), is a value that the researcher sets in advance as the threshold for statistical significance. It is the maximum risk of making a false positive conclusion ( Type I error ) that you are willing to accept .

In a hypothesis test, the  p value is compared to the significance level to decide whether to reject the null hypothesis.

• If the p value is  higher than the significance level, the null hypothesis is not refuted, and the results are not statistically significant .
• If the p value is lower than the significance level, the results are interpreted as refuting the null hypothesis and reported as statistically significant .

Usually, the significance level is set to 0.05 or 5%. That means your results must have a 5% or lower chance of occurring under the null hypothesis to be considered statistically significant.

The significance level can be lowered for a more conservative test. That means an effect has to be larger to be considered statistically significant.

The significance level may also be set higher for significance testing in non-academic marketing or business contexts. This makes the study less rigorous and increases the probability of finding a statistically significant result.

As best practice, you should set a significance level before you begin your study. Otherwise, you can easily manipulate your results to match your research predictions.

It’s important to note that hypothesis testing can only show you whether or not to reject the null hypothesis in favor of the alternative hypothesis. It can never “prove” the null hypothesis, because the lack of a statistically significant effect doesn’t mean that absolutely no effect exists.

When reporting statistical significance, include relevant descriptive statistics about your data (e.g., means and standard deviations ) as well as the test statistic and p value.

There are various critiques of the concept of statistical significance and how it is used in research.

Researchers classify results as statistically significant or non-significant using a conventional threshold that lacks any theoretical or practical basis. This means that even a tiny 0.001 decrease in a p value can convert a research finding from statistically non-significant to significant with almost no real change in the effect.

On its own, statistical significance may also be misleading because it’s affected by sample size. In extremely large samples , you’re more likely to obtain statistically significant results, even if the effect is actually small or negligible in the real world. This means that small effects are often exaggerated if they meet the significance threshold, while interesting results are ignored when they fall short of meeting the threshold.

The strong emphasis on statistical significance has led to a serious publication bias and replication crisis in the social sciences and medicine over the last few decades. Results are usually only published in academic journals if they show statistically significant results—but statistically significant results often can’t be reproduced in high quality replication studies.

As a result, many scientists call for retiring statistical significance as a decision-making tool in favor of more nuanced approaches to interpreting results.

That’s why APA guidelines advise reporting not only p values but also  effect sizes and confidence intervals wherever possible to show the real world implications of a research outcome.

Aside from statistical significance, clinical significance and practical significance are also important research outcomes.

Practical significance shows you whether the research outcome is important enough to be meaningful in the real world. It’s indicated by the effect size of the study.

Clinical significance is relevant for intervention and treatment studies. A treatment is considered clinically significant when it tangibly or substantially improves the lives of patients.

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Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test . Significance is usually denoted by a p -value , or probability value.

Statistical significance is arbitrary – it depends on the threshold, or alpha value, chosen by the researcher. The most common threshold is p < 0.05, which means that the data is likely to occur less than 5% of the time under the null hypothesis .

When the p -value falls below the chosen alpha value, then we say the result of the test is statistically significant.

A p -value , or probability value, is a number describing how likely it is that your data would have occurred under the null hypothesis of your statistical test .

P -values are usually automatically calculated by the program you use to perform your statistical test. They can also be estimated using p -value tables for the relevant test statistic .

P -values are calculated from the null distribution of the test statistic. They tell you how often a test statistic is expected to occur under the null hypothesis of the statistical test, based on where it falls in the null distribution.

If the test statistic is far from the mean of the null distribution, then the p -value will be small, showing that the test statistic is not likely to have occurred under the null hypothesis.

No. The p -value only tells you how likely the data you have observed is to have occurred under the null hypothesis .

If the p -value is below your threshold of significance (typically p < 0.05), then you can reject the null hypothesis, but this does not necessarily mean that your alternative hypothesis is true.

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• A Researcher’s Guide To Statistical Significance And Sample Size Calculations

What Does It Mean for Research to Be Statistically Significant?

Quick Navigation:

What does it mean to be statistically significant, an example of null hypothesis significance testing, measuring statistical significance: understanding the p value (significance level), what factors affect the power of hypothesis test, 1. sample size, 2. significance level, 3. standard deviations, 4. effect size, why is statistical significance important for researchers, does your study need to be statistically significant, practical significance vs. statistical significance, part 1: how is statistical significance defined in research.

The world today is drowning in data.

That may sound like hyperbole but consider this. In 2018, humans around the world produced more than 2.5 quintillion bytes of data—each day. According to some estimates , every minute people conduct almost 4.5 million Google searches, post 511,200 tweets, watch 4.5 million YouTube videos, swipe 1.4 million times on Tinder, and order 8,683 meals from GrubHub. These numbers—and the world’s total data—are expected to continue growing exponentially in the coming years.

For behavioral researchers and businesses, this data represents a valuable opportunity. However, using data to learn about human behavior or make decisions about consumer behavior often requires an understanding of statistics and statistical significance.

Statistical significance is a measurement of how likely it is that the difference between two groups, models, or statistics occurred by chance or occurred because two variables are actually related to each other. This means that a “statistically significant” finding is one in which it is likely the finding is real, reliable, and not due to chance.

To evaluate whether a finding is statistically significant, researchers engage in a process known as null hypothesis significance testing . Null hypothesis significance testing is less of a mathematical formula and more of a logical process for thinking about the strength and legitimacy of a finding.

Imagine a Vice President of Marketing asks her team to test a new layout for the company website. The new layout streamlines the user experience by making it easier for people to place orders and suggesting additional items to go along with each customer’s purchase. After testing the new website, the VP finds that visitors to the site spend an average of $12.63. Under the old layout, visitors spent an average of $12.32, meaning the new layout increases average spending by $0.31 per person. The question the VP must answer is whether the difference of $0.31 per person is significant or something that likely occurred by chance.

To answer this question with statistical analysis, the VP begins by adopting a skeptical stance toward her data known as the null hypothesis . The null hypothesis assumes that whatever researchers are studying does not actually exist in the population of interest. So, in this case the VP assumes that the change in website layout does not influence how much people spend on purchases.

With the null hypothesis in mind, the manager asks how likely it is that she would obtain the results observed in her study—the average difference of $0.31 per visitor—if the change in website layout actually causes no difference in people’s spending (i.e., if the null hypothesis is true). If the probability of obtaining the observed results is low, the manager will reject the null hypothesis and conclude that her finding is statistically significant.

Statistically significant findings indicate not only that the researchers’ results are unlikely the result of chance, but also that there is an effect or relationship between the variables being studied in the larger population. However, because researchers want to ensure they do not falsely conclude there is a meaningful difference between groups when in fact the difference is due to chance, they often set stringent criteria for their statistical tests. This criterion is known as the significance level .

Within the social sciences, researchers often adopt a significance level of 5%. This means researchers are only willing to conclude that the results of their study are statistically significant if the probability of obtaining those results if the null hypothesis were true—known as the p value —is less than 5%.

Five percent represents a stringent criterion, but there is nothing magical about it. In medical research, significance levels are often set at 1%. In cognitive neuroscience, researchers often adopt significance levels well below 1%. And, when astronomers seek to explain aspects of the universe or physicists study new particles like the Higgs Boson they set significance levels several orders of magnitude below .05.

In other research contexts like business or industry, researchers may set more lenient significance levels depending on the aim of their research. However, in all research, the more stringently a researcher sets their significance level, the more confident they can be that their results are not due to chance.

Determining whether a given set of results is statistically significant is only one half of the hypothesis testing equation. The other half is ensuring that the statistical tests a researcher conducts are powerful enough to detect an effect if one really exists. That is, when a researcher concludes their hypothesis was incorrect and there is no effect between the variables being studied, that conclusion is only meaningful if the study was powerful enough to detect an effect if one really existed.

The power of a hypothesis test is influenced by several factors.

Sample size—or, the number of participants the researcher collects data from—affects the power of a hypothesis test. Larger samples with more observations generally lead to higher-powered tests than smaller samples. In addition, large samples are more likely to produce replicable results because extreme scores that occur by chance are more likely to balance out in a large sample rather than in a small one.

Although setting a low significance level helps researchers ensure their results are not due to chance, it also lowers their power to detect an effect because it makes rejecting the null hypothesis harder. In this respect, the significance level a researcher selects is often in competition with power.

Standard deviations represent unexplained variability within data, also known as error. Generally speaking, the more unexplained variability within a dataset, the less power researchers have to detect an effect. Unexplained variability can be the result of measurement error, individual differences among participants, or situational noise.   

A final factor that influences power is the size of the effect a researcher is studying. As you might expect, big changes in behavior are easier to detect than small ones.

Sometimes researchers do not know the strength of an effect before conducting a study. Even though this makes it harder to conduct a well powered study, it is important to keep in mind that phenomena that produce a large effect will lead to studies with more power than phenomena that produce only a small effect.

Statistical significance is important because it allows researchers to hold a degree of confidence that their findings are real, reliable, and not due to chance. But statistical significance is not equally important to all researchers in all situations. The importance of obtaining statistically significant results depends on what a researcher studies and within what context.

Within academic research, statistical significance is often critical because academic researchers study theoretical relationships between different variables and behavior. Furthermore, the goal of academic research is often to publish research reports in scientific journals. The threshold for publishing in academic journals is often a series of statistically significant results.

Outside of academia, statistical significance is often less important. Researchers, managers, and decision makers in business may use statistical significance to understand how strongly the results of a study should inform the decisions they make. But, because statistical significance is simply a way of quantifying how much confidence to hold in a research finding, people in industry are often more interested in a finding’s practical significance than statistical significance.

To demonstrate the difference between practical and statistical significance, imagine you’re a candidate for political office. Maybe you have decided to run for local or state-wide office, or, if you’re feeling bold, imagine you’re running for President.

During your campaign, your team comes to you with data on messages intended to mobilize voters. These messages have been market tested and now you and your team must decide which ones to adopt.

If you go with Message A, 41% of registered voters say they are likely to turn out at the polls and cast a ballot. If you go with Message B, this number drops to 37%. As a candidate, should you care whether this difference is statistically significant at a p value below .05?

The answer is of course not. What you likely care about more than statistical significance is practical significance —the likelihood that the difference between groups is large enough to be meaningful in real life.  

You should ensure there is some rigor behind the difference in messages before you spend money on a marketing campaign, but when elections are sometimes decided by as little as one vote you should adopt the message that brings more people out to vote. Within business and industry, the practical significance of a research finding is often equally if not more important than the statistical significance. In addition, when findings have large practical significance, they are almost always statistically significant too.

Conducting statistically significant research is a challenge, but it’s a challenge worth tackling. Flawed data and faulty analyses only lead to poor decisions. Start taking steps to ensure your surveys and experiments produce valid results by using CloudResearch. If you have the team to conduct your own studies, CloudResearch can help you find large samples of online participants quickly and easily. Regardless of your demographic criteria or sample size, we can help you get the participants you need. If your team doesn’t have the resources to run a study, we can run it for you. Our team of expert social scientists, computer scientists, and software engineers can design any study, collect the data, and analyze the results for you. Let us show you how conducting statistically significant research can improve your decision-making today.

Continue Reading: A Researcher’s Guide to Statistical Significance and Sample Size Calculations

Part 2: How to Calculate Statistical Significance

Part 3: Determining Sample Size: How Many Survey Participants Do You Need?

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Hypothesis testing, p values, confidence intervals, and significance.

Jacob Shreffler ; Martin R. Huecker .

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Last Update: March 13, 2023 .

• Definition/Introduction

Medical providers often rely on evidence-based medicine to guide decision-making in practice. Often a research hypothesis is tested with results provided, typically with p values, confidence intervals, or both. Additionally, statistical or research significance is estimated or determined by the investigators. Unfortunately, healthcare providers may have different comfort levels in interpreting these findings, which may affect the adequate application of the data.

• Issues of Concern

Without a foundational understanding of hypothesis testing, p values, confidence intervals, and the difference between statistical and clinical significance, it may affect healthcare providers' ability to make clinical decisions without relying purely on the research investigators deemed level of significance. Therefore, an overview of these concepts is provided to allow medical professionals to use their expertise to determine if results are reported sufficiently and if the study outcomes are clinically appropriate to be applied in healthcare practice.

Hypothesis Testing

Investigators conducting studies need research questions and hypotheses to guide analyses. Starting with broad research questions (RQs), investigators then identify a gap in current clinical practice or research. Any research problem or statement is grounded in a better understanding of relationships between two or more variables. For this article, we will use the following research question example:

Research Question: Is Drug 23 an effective treatment for Disease A?

Research questions do not directly imply specific guesses or predictions; we must formulate research hypotheses. A hypothesis is a predetermined declaration regarding the research question in which the investigator(s) makes a precise, educated guess about a study outcome. This is sometimes called the alternative hypothesis and ultimately allows the researcher to take a stance based on experience or insight from medical literature. An example of a hypothesis is below.

Research Hypothesis: Drug 23 will significantly reduce symptoms associated with Disease A compared to Drug 22.

The null hypothesis states that there is no statistical difference between groups based on the stated research hypothesis.

Researchers should be aware of journal recommendations when considering how to report p values, and manuscripts should remain internally consistent.

Regarding p values, as the number of individuals enrolled in a study (the sample size) increases, the likelihood of finding a statistically significant effect increases. With very large sample sizes, the p-value can be very low significant differences in the reduction of symptoms for Disease A between Drug 23 and Drug 22. The null hypothesis is deemed true until a study presents significant data to support rejecting the null hypothesis. Based on the results, the investigators will either reject the null hypothesis (if they found significant differences or associations) or fail to reject the null hypothesis (they could not provide proof that there were significant differences or associations).

To test a hypothesis, researchers obtain data on a representative sample to determine whether to reject or fail to reject a null hypothesis. In most research studies, it is not feasible to obtain data for an entire population. Using a sampling procedure allows for statistical inference, though this involves a certain possibility of error. [1]  When determining whether to reject or fail to reject the null hypothesis, mistakes can be made: Type I and Type II errors. Though it is impossible to ensure that these errors have not occurred, researchers should limit the possibilities of these faults. [2]

Significance

Significance is a term to describe the substantive importance of medical research. Statistical significance is the likelihood of results due to chance. [3]  Healthcare providers should always delineate statistical significance from clinical significance, a common error when reviewing biomedical research. [4]  When conceptualizing findings reported as either significant or not significant, healthcare providers should not simply accept researchers' results or conclusions without considering the clinical significance. Healthcare professionals should consider the clinical importance of findings and understand both p values and confidence intervals so they do not have to rely on the researchers to determine the level of significance. [5]  One criterion often used to determine statistical significance is the utilization of p values.

P values are used in research to determine whether the sample estimate is significantly different from a hypothesized value. The p-value is the probability that the observed effect within the study would have occurred by chance if, in reality, there was no true effect. Conventionally, data yielding a p<0.05 or p<0.01 is considered statistically significant. While some have debated that the 0.05 level should be lowered, it is still universally practiced. [6]  Hypothesis testing allows us to determine the size of the effect.

An example of findings reported with p values are below:

Statement: Drug 23 reduced patients' symptoms compared to Drug 22. Patients who received Drug 23 (n=100) were 2.1 times less likely than patients who received Drug 22 (n = 100) to experience symptoms of Disease A, p<0.05.

Statement:Individuals who were prescribed Drug 23 experienced fewer symptoms (M = 1.3, SD = 0.7) compared to individuals who were prescribed Drug 22 (M = 5.3, SD = 1.9). This finding was statistically significant, p= 0.02.

For either statement, if the threshold had been set at 0.05, the null hypothesis (that there was no relationship) should be rejected, and we should conclude significant differences. Noticeably, as can be seen in the two statements above, some researchers will report findings with < or > and others will provide an exact p-value (0.000001) but never zero [6] . When examining research, readers should understand how p values are reported. The best practice is to report all p values for all variables within a study design, rather than only providing p values for variables with significant findings. [7]  The inclusion of all p values provides evidence for study validity and limits suspicion for selective reporting/data mining.  

While researchers have historically used p values, experts who find p values problematic encourage the use of confidence intervals. [8] . P-values alone do not allow us to understand the size or the extent of the differences or associations. [3]  In March 2016, the American Statistical Association (ASA) released a statement on p values, noting that scientific decision-making and conclusions should not be based on a fixed p-value threshold (e.g., 0.05). They recommend focusing on the significance of results in the context of study design, quality of measurements, and validity of data. Ultimately, the ASA statement noted that in isolation, a p-value does not provide strong evidence. [9]

When conceptualizing clinical work, healthcare professionals should consider p values with a concurrent appraisal study design validity. For example, a p-value from a double-blinded randomized clinical trial (designed to minimize bias) should be weighted higher than one from a retrospective observational study [7] . The p-value debate has smoldered since the 1950s [10] , and replacement with confidence intervals has been suggested since the 1980s. [11]

Confidence Intervals

A confidence interval provides a range of values within given confidence (e.g., 95%), including the accurate value of the statistical constraint within a targeted population. [12]  Most research uses a 95% CI, but investigators can set any level (e.g., 90% CI, 99% CI). [13]  A CI provides a range with the lower bound and upper bound limits of a difference or association that would be plausible for a population. [14]  Therefore, a CI of 95% indicates that if a study were to be carried out 100 times, the range would contain the true value in 95, [15]  confidence intervals provide more evidence regarding the precision of an estimate compared to p-values. [6]

In consideration of the similar research example provided above, one could make the following statement with 95% CI:

Statement: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22; there was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).

It is important to note that the width of the CI is affected by the standard error and the sample size; reducing a study sample number will result in less precision of the CI (increase the width). [14]  A larger width indicates a smaller sample size or a larger variability. [16]  A researcher would want to increase the precision of the CI. For example, a 95% CI of 1.43 – 1.47 is much more precise than the one provided in the example above. In research and clinical practice, CIs provide valuable information on whether the interval includes or excludes any clinically significant values. [14]

Null values are sometimes used for differences with CI (zero for differential comparisons and 1 for ratios). However, CIs provide more information than that. [15]  Consider this example: A hospital implements a new protocol that reduced wait time for patients in the emergency department by an average of 25 minutes (95% CI: -2.5 – 41 minutes). Because the range crosses zero, implementing this protocol in different populations could result in longer wait times; however, the range is much higher on the positive side. Thus, while the p-value used to detect statistical significance for this may result in "not significant" findings, individuals should examine this range, consider the study design, and weigh whether or not it is still worth piloting in their workplace.

Similarly to p-values, 95% CIs cannot control for researchers' errors (e.g., study bias or improper data analysis). [14]  In consideration of whether to report p-values or CIs, researchers should examine journal preferences. When in doubt, reporting both may be beneficial. [13]  An example is below:

Reporting both: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22, p = 0.009. There was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).

• Clinical Significance

Recall that clinical significance and statistical significance are two different concepts. Healthcare providers should remember that a study with statistically significant differences and large sample size may be of no interest to clinicians, whereas a study with smaller sample size and statistically non-significant results could impact clinical practice. [14]  Additionally, as previously mentioned, a non-significant finding may reflect the study design itself rather than relationships between variables.

Healthcare providers using evidence-based medicine to inform practice should use clinical judgment to determine the practical importance of studies through careful evaluation of the design, sample size, power, likelihood of type I and type II errors, data analysis, and reporting of statistical findings (p values, 95% CI or both). [4]  Interestingly, some experts have called for "statistically significant" or "not significant" to be excluded from work as statistical significance never has and will never be equivalent to clinical significance. [17]

The decision on what is clinically significant can be challenging, depending on the providers' experience and especially the severity of the disease. Providers should use their knowledge and experiences to determine the meaningfulness of study results and make inferences based not only on significant or insignificant results by researchers but through their understanding of study limitations and practical implications.

• Nursing, Allied Health, and Interprofessional Team Interventions

All physicians, nurses, pharmacists, and other healthcare professionals should strive to understand the concepts in this chapter. These individuals should maintain the ability to review and incorporate new literature for evidence-based and safe care. 

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Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Shreffler J, Huecker MR. Hypothesis Testing, P Values, Confidence Intervals, and Significance. [Updated 2023 Mar 13]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Quantitative Research

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• First Online: 13 January 2019
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• Leigh A. Wilson 2 , 3  

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Writing Quantitative Research Studies

Qualitative Research Methods

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Wilson, L.A. (2019). Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_54

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Quantitative and Qualitative Research

• I NEED TO . . .

What is Quantitative Research?

• What is Qualitative Research?
• Quantitative vs Qualitative
• 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
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• What Kind of Article is This?
• More Research Help This link opens in a new window

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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A Quick Guide to Quantitative Research in the Social Sciences

(12 reviews)

Christine Davies, Carmarthen, Wales

Copyright Year: 2020

Last Update: 2021

Publisher: University of Wales Trinity Saint David

Language: English

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Reviewed by Jennifer Taylor, Assistant Professor, Texas A&M University-Corpus Christi on 4/18/24

This resource is a quick guide to quantitative research in the social sciences and not a comprehensive resource. It provides a VERY general overview of quantitative research but offers a good starting place for students new to research. It... read more

Comprehensiveness rating: 4 see less

This resource is a quick guide to quantitative research in the social sciences and not a comprehensive resource. It provides a VERY general overview of quantitative research but offers a good starting place for students new to research. It offers links and references to additional resources that are more comprehensive in nature.

Content Accuracy rating: 4

The content is relatively accurate. The measurement scale section is very sparse. Not all types of research designs or statistical methods are included, but it is a guide, so details are meant to be limited.

Relevance/Longevity rating: 4

The examples were interesting and appropriate. The content is up to date and will be useful for several years.

Clarity rating: 5

The text was clearly written. Tables and figures are not referenced in the text, which would have been nice.

Consistency rating: 5

The framework is consistent across chapters with terminology clearly highlighted and defined.

Modularity rating: 5

The chapters are subdivided into section that can be divided and assigned as reading in a course. Most chapters are brief and concise, unless elaboration is necessary, such as with the data analysis chapter. Again, this is a guide and not a comprehensive text, so sections are shorter and don't always include every subtopic that may be considered.

Organization/Structure/Flow rating: 5

The guide is well organized. I appreciate that the topics are presented in a logical and clear manner. The topics are provided in an order consistent with traditional research methods.

Interface rating: 5

The interface was easy to use and navigate. The images were clear and easy to read.

Grammatical Errors rating: 5

I did not notice any grammatical errors.

Cultural Relevance rating: 5

The materials are not culturally insensitive or offensive in any way.

I teach a Marketing Research course to undergraduates. I would consider using some of the chapters or topics included, especially the overview of the research designs and the analysis of data section.

Reviewed by Tiffany Kindratt, Assistant Professor, University of Texas at Arlington on 3/9/24

The text provides a brief overview of quantitative research topics that is geared towards research in the fields of education, sociology, business, and nursing. The author acknowledges that the textbook is not a comprehensive resource but offers... read more

Comprehensiveness rating: 3 see less

The text provides a brief overview of quantitative research topics that is geared towards research in the fields of education, sociology, business, and nursing. The author acknowledges that the textbook is not a comprehensive resource but offers references to other resources that can be used to deepen the knowledge. The text does not include a glossary or index. The references in the figures for each chapter are not included in the reference section. It would be helpful to include those.

Overall, the text is accurate. For example, Figure 1 on page 6 provides a clear overview of the research process. It includes general definitions of primary and secondary research. It would be helpful to include more details to explain some of the examples before they are presented. For instance, the example on page 5 was unclear how it pertains to the literature review section.

In general, the text is relevant and up-to-date. The text includes many inferences of moving from qualitative to quantitative analysis. This was surprising to me as a quantitative researcher. The author mentions that moving from a qualitative to quantitative approach should only be done when needed. As a predominantly quantitative researcher, I would not advice those interested in transitioning to using a qualitative approach that qualitative research would enhance their research—not something that should only be done if you have to.

Clarity rating: 4

The text is written in a clear manner. It would be helpful to the reader if there was a description of the tables and figures in the text before they are presented.

Consistency rating: 4

The framework for each chapter and terminology used are consistent.

Modularity rating: 4

The text is clearly divided into sections within each chapter. Overall, the chapters are a similar brief length except for the chapter on data analysis, which is much more comprehensive than others.

Organization/Structure/Flow rating: 4

The topics in the text are presented in a clear and logical order. The order of the text follows the conventional research methodology in social sciences.

I did not encounter any interface issues when reviewing this text. All links worked and there were no distortions of the images or charts that may confuse the reader.

Grammatical Errors rating: 3

There are some grammatical/typographical errors throughout. Of note, for Section 5 in the table of contents. “The” should be capitalized to start the title. In the title for Table 3, the “t” in typical should be capitalized.

Cultural Relevance rating: 4

The examples are culturally relevant. The text is geared towards learners in the UK, but examples are relevant for use in other countries (i.e., United States). I did not see any examples that may be considered culturally insensitive or offensive in any way.

I teach a course on research methods in a Bachelor of Science in Public Health program. I would consider using some of the text, particularly in the analysis chapter to supplement the current textbook in the future.

Reviewed by Finn Bell, Assistant Professor, University of Michigan, Dearborn on 1/3/24

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary. read more

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary.

Content Accuracy rating: 5

As far as I can tell, the text is accurate, error-free and unbiased.

Relevance/Longevity rating: 5

This text is up-to-date, and given the content, unlikely to become obsolete any time soon.

The text is very clear and accessible.

The text is internally consistent.

Given how short the text is, it seems unnecessary to divide it into smaller readings, nonetheless, it is clearly labelled such that an instructor could do so.

The text is well-organized and brings readers through basic quantitative methods in a logical, clear fashion.

Easy to navigate. Only one table that is split between pages, but not in a way that is confusing.

There were no noticeable grammatical errors.

The examples in this book don't give enough information to rate this effectively.

This text is truly a very quick guide at only 26 double-spaced pages. Nonetheless, Davies packs a lot of information on the basics of quantitative research methods into this text, in an engaging way with many examples of the concepts presented. This guide is more of a brief how-to that takes readers as far as how to select statistical tests. While it would be impossible to fully learn quantitative research from such a short text, of course, this resource provides a great introduction, overview, and refresher for program evaluation courses.

Reviewed by Shari Fedorowicz, Adjunct Professor, Bridgewater State University on 12/16/22

The text is indeed a quick guide for utilizing quantitative research. Appropriate and effective examples and diagrams were used throughout the text. The author clearly differentiates between use of quantitative and qualitative research providing... read more

Comprehensiveness rating: 5 see less

The text is indeed a quick guide for utilizing quantitative research. Appropriate and effective examples and diagrams were used throughout the text. The author clearly differentiates between use of quantitative and qualitative research providing the reader with the ability to distinguish two terms that frequently get confused. In addition, links and outside resources are provided to deepen the understanding as an option for the reader. The use of these links, coupled with diagrams and examples make this text comprehensive.

The content is mostly accurate. Given that it is a quick guide, the author chose a good selection of which types of research designs to include. However, some are not provided. For example, correlational or cross-correlational research is omitted and is not discussed in Section 3, but is used as a statistical example in the last section.

Examples utilized were appropriate and associated with terms adding value to the learning. The tables that included differentiation between types of statistical tests along with a parametric/nonparametric table were useful and relevant.

The purpose to the text and how to use this guide book is stated clearly and is established up front. The author is also very clear regarding the skill level of the user. Adding to the clarity are the tables with terms, definitions, and examples to help the reader unpack the concepts. The content related to the terms was succinct, direct, and clear. Many times examples or figures were used to supplement the narrative.

The text is consistent throughout from contents to references. Within each section of the text, the introductory paragraph under each section provides a clear understanding regarding what will be discussed in each section. The layout is consistent for each section and easy to follow.

The contents are visible and address each section of the text. A total of seven sections, including a reference section, is in the contents. Each section is outlined by what will be discussed in the contents. In addition, within each section, a heading is provided to direct the reader to the subtopic under each section.

The text is well-organized and segues appropriately. I would have liked to have seen an introductory section giving a narrative overview of what is in each section. This would provide the reader with the ability to get a preliminary glimpse into each upcoming sections and topics that are covered.

The book was easy to navigate and well-organized. Examples are presented in one color, links in another and last, figures and tables. The visuals supplemented the reading and placed appropriately. This provides an opportunity for the reader to unpack the reading by use of visuals and examples.

No significant grammatical errors.

The text is not offensive or culturally insensitive. Examples were inclusive of various races, ethnicities, and backgrounds.

This quick guide is a beneficial text to assist in unpacking the learning related to quantitative statistics. I would use this book to complement my instruction and lessons, or use this book as a main text with supplemental statistical problems and formulas. References to statistical programs were appropriate and were useful. The text did exactly what was stated up front in that it is a direct guide to quantitative statistics. It is well-written and to the point with content areas easy to locate by topic.

Reviewed by Sarah Capello, Assistant Professor, Radford University on 1/18/22

The text claims to provide "quick and simple advice on quantitative aspects of research in social sciences," which it does. There is no index or glossary, although vocabulary words are bolded and defined throughout the text. read more

The text claims to provide "quick and simple advice on quantitative aspects of research in social sciences," which it does. There is no index or glossary, although vocabulary words are bolded and defined throughout the text.

The content is mostly accurate. I would have preferred a few nuances to be hashed out a bit further to avoid potential reader confusion or misunderstanding of the concepts presented.

The content is current; however, some of the references cited in the text are outdated. Newer editions of those texts exist.

The text is very accessible and readable for a variety of audiences. Key terms are well-defined.

There are no content discrepancies within the text. The author even uses similarly shaped graphics for recurring purposes throughout the text (e.g., arrow call outs for further reading, rectangle call outs for examples).

The content is chunked nicely by topics and sections. If it were used for a course, it would be easy to assign different sections of the text for homework, etc. without confusing the reader if the instructor chose to present the content in a different order.

The author follows the structure of the research process. The organization of the text is easy to follow and comprehend.

All of the supplementary images (e.g., tables and figures) were beneficial to the reader and enhanced the text.

There are no significant grammatical errors.

I did not find any culturally offensive or insensitive references in the text.

This text does the difficult job of introducing the complicated concepts and processes of quantitative research in a quick and easy reference guide fairly well. I would not depend solely on this text to teach students about quantitative research, but it could be a good jumping off point for those who have no prior knowledge on this subject or those who need a gentle introduction before diving in to more advanced and complex readings of quantitative research methods.

Reviewed by J. Marlie Henry, Adjunct Faculty, University of Saint Francis on 12/9/21

Considering the length of this guide, this does a good job of addressing major areas that typically need to be addressed. There is a contents section. The guide does seem to be organized accordingly with appropriate alignment and logical flow of... read more

Considering the length of this guide, this does a good job of addressing major areas that typically need to be addressed. There is a contents section. The guide does seem to be organized accordingly with appropriate alignment and logical flow of thought. There is no glossary but, for a guide of this length, a glossary does not seem like it would enhance the guide significantly.

The content is relatively accurate. Expanding the content a bit more or explaining that the methods and designs presented are not entirely inclusive would help. As there are different schools of thought regarding what should/should not be included in terms of these designs and methods, simply bringing attention to that and explaining a bit more would help.

Relevance/Longevity rating: 3

This content needs to be updated. Most of the sources cited are seven or more years old. Even more, it would be helpful to see more currently relevant examples. Some of the source authors such as Andy Field provide very interesting and dynamic instruction in general, but they have much more current information available.

The language used is clear and appropriate. Unnecessary jargon is not used. The intent is clear- to communicate simply in a straightforward manner.

The guide seems to be internally consistent in terms of terminology and framework. There do not seem to be issues in this area. Terminology is internally consistent.

For a guide of this length, the author structured this logically into sections. This guide could be adopted in whole or by section with limited modifications. Courses with fewer than seven modules could also logically group some of the sections.

This guide does present with logical organization. The topics presented are conceptually sequenced in a manner that helps learners build logically on prior conceptualization. This also provides a simple conceptual framework for instructors to guide learners through the process.

Interface rating: 4

The visuals themselves are simple, but they are clear and understandable without distracting the learner. The purpose is clear- that of learning rather than visuals for the sake of visuals. Likewise, navigation is clear and without issues beyond a broken link (the last source noted in the references).

This guide seems to be free of grammatical errors.

It would be interesting to see more cultural integration in a guide of this nature, but the guide is not culturally insensitive or offensive in any way. The language used seems to be consistent with APA's guidelines for unbiased language.

Reviewed by Heng Yu-Ku, Professor, University of Northern Colorado on 5/13/21

The text covers all areas and ideas appropriately and provides practical tables, charts, and examples throughout the text. I would suggest the author also provides a complete research proposal at the end of Section 3 (page 10) and a comprehensive... read more

The text covers all areas and ideas appropriately and provides practical tables, charts, and examples throughout the text. I would suggest the author also provides a complete research proposal at the end of Section 3 (page 10) and a comprehensive research study as an Appendix after section 7 (page 26) to help readers comprehend information better.

For the most part, the content is accurate and unbiased. However, the author only includes four types of research designs used on the social sciences that contain quantitative elements: 1. Mixed method, 2) Case study, 3) Quasi-experiment, and 3) Action research. I wonder why the correlational research is not included as another type of quantitative research design as it has been introduced and emphasized in section 6 by the author.

I believe the content is up-to-date and that necessary updates will be relatively easy and straightforward to implement.

The text is easy to read and provides adequate context for any technical terminology used. However, the author could provide more detailed information about estimating the minimum sample size but not just refer the readers to use the online sample calculators at a different website.

The text is internally consistent in terms of terminology and framework. The author provides the right amount of information with additional information or resources for the readers.

The text includes seven sections. Therefore, it is easier for the instructor to allocate or divide the content into different weeks of instruction within the course.

Yes, the topics in the text are presented in a logical and clear fashion. The author provides clear and precise terminologies, summarizes important content in Table or Figure forms, and offers examples in each section for readers to check their understanding.

The interface of the book is consistent and clear, and all the images and charts provided in the book are appropriate. However, I did encounter some navigation problems as a couple of links are not working or requires permission to access those (pages 10 and 27).

No grammatical errors were found.

No culturally incentive or offensive in its language and the examples provided were found.

As the book title stated, this book provides “A Quick Guide to Quantitative Research in Social Science. It offers easy-to-read information and introduces the readers to the research process, such as research questions, research paradigms, research process, research designs, research methods, data collection, data analysis, and data discussion. However, some links are not working or need permissions to access them (pages 10 and 27).

Reviewed by Hsiao-Chin Kuo, Assistant Professor, Northeastern Illinois University on 4/26/21, updated 4/28/21

As a quick guide, it covers basic concepts related to quantitative research. It starts with WHY quantitative research with regard to asking research questions and considering research paradigms, then provides an overview of research design and... read more

As a quick guide, it covers basic concepts related to quantitative research. It starts with WHY quantitative research with regard to asking research questions and considering research paradigms, then provides an overview of research design and process, discusses methods, data collection and analysis, and ends with writing a research report. It also identifies its target readers/users as those begins to explore quantitative research. It would be helpful to include more examples for readers/users who are new to quantitative research.

Its content is mostly accurate and no bias given its nature as a quick guide. Yet, it is also quite simplified, such as its explanations of mixed methods, case study, quasi-experimental research, and action research. It provides resources for extended reading, yet more recent works will be helpful.

The book is relevant given its nature as a quick guide. It would be helpful to provide more recent works in its resources for extended reading, such as the section for Survey Research (p. 12). It would also be helpful to include more information to introduce common tools and software for statistical analysis.

The book is written with clear and understandable language. Important terms and concepts are presented with plain explanations and examples. Figures and tables are also presented to support its clarity. For example, Table 4 (p. 20) gives an easy-to-follow overview of different statistical tests.

The framework is very consistent with key points, further explanations, examples, and resources for extended reading. The sample studies are presented following the layout of the content, such as research questions, design and methods, and analysis. These examples help reinforce readers' understanding of these common research elements.

The book is divided into seven chapters. Each chapter clearly discusses an aspect of quantitative research. It can be easily divided into modules for a class or for a theme in a research method class. Chapters are short and provides additional resources for extended reading.

The topics in the chapters are presented in a logical and clear structure. It is easy to follow to a degree. Though, it would be also helpful to include the chapter number and title in the header next to its page number.

The text is easy to navigate. Most of the figures and tables are displayed clearly. Yet, there are several sections with empty space that is a bit confusing in the beginning. Again, it can be helpful to include the chapter number/title next to its page number.

Grammatical Errors rating: 4

No major grammatical errors were found.

There are no cultural insensitivities noted.

Given the nature and purpose of this book, as a quick guide, it provides readers a quick reference for important concepts and terms related to quantitative research. Because this book is quite short (27 pages), it can be used as an overview/preview about quantitative research. Teacher's facilitation/input and extended readings will be needed for a deeper learning and discussion about aspects of quantitative research.

Reviewed by Yang Cheng, Assistant Professor, North Carolina State University on 1/6/21

It covers the most important topics such as research progress, resources, measurement, and analysis of the data. read more

It covers the most important topics such as research progress, resources, measurement, and analysis of the data.

The book accurately describes the types of research methods such as mixed-method, quasi-experiment, and case study. It talks about the research proposal and key differences between statistical analyses as well.

The book pinpointed the significance of running a quantitative research method and its relevance to the field of social science.

The book clearly tells us the differences between types of quantitative methods and the steps of running quantitative research for students.

The book is consistent in terms of terminologies such as research methods or types of statistical analysis.

It addresses the headlines and subheadlines very well and each subheading should be necessary for readers.

The book was organized very well to illustrate the topic of quantitative methods in the field of social science.

The pictures within the book could be further developed to describe the key concepts vividly.

The textbook contains no grammatical errors.

It is not culturally offensive in any way.

Overall, this is a simple and quick guide for this important topic. It should be valuable for undergraduate students who would like to learn more about research methods.

Reviewed by Pierre Lu, Associate Professor, University of Texas Rio Grande Valley on 11/20/20

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas. read more

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas.

Mostly accurate content.

As a quick guide, content is highly relevant.

Succinct and clear.

Internally, the text is consistent in terms of terminology used.

The text is easily and readily divisible into smaller sections that can be used as assignments.

I like that there are examples throughout the book.

Easy to read. No interface/ navigation problems.

No grammatical errors detected.

I am not aware of the culturally insensitive description. After all, this is a methodology book.

I think the book has potential to be adopted as a foundation for quantitative research courses, or as a review in the first weeks in advanced quantitative course.

Reviewed by Sarah Fischer, Assistant Professor, Marymount University on 7/31/20

It is meant to be an overview, but it incredibly condensed and spends almost no time on key elements of statistics (such as what makes research generalizable, or what leads to research NOT being generalizable). read more

It is meant to be an overview, but it incredibly condensed and spends almost no time on key elements of statistics (such as what makes research generalizable, or what leads to research NOT being generalizable).

Content Accuracy rating: 1

Contains VERY significant errors, such as saying that one can "accept" a hypothesis. (One of the key aspect of hypothesis testing is that one either rejects or fails to reject a hypothesis, but NEVER accepts a hypothesis.)

Very relevant to those experiencing the research process for the first time. However, it is written by someone working in the natural sciences but is a text for social sciences. This does not explain the errors, but does explain why sometimes the author assumes things about the readers ("hail from more subjectivist territory") that are likely not true.

Clarity rating: 3

Some statistical terminology not explained clearly (or accurately), although the author has made attempts to do both.

Very consistently laid out.

Chapters are very short yet also point readers to outside texts for additional information. Easy to follow.

Generally logically organized.

Easy to navigate, images clear. The additional sources included need to linked to.

Minor grammatical and usage errors throughout the text.

Makes efforts to be inclusive.

The idea of this book is strong--short guides like this are needed. However, this book would likely be strengthened by a revision to reduce inaccuracies and improve the definitions and technical explanations of statistical concepts. Since the book is specifically aimed at the social sciences, it would also improve the text to have more examples that are based in the social sciences (rather than the health sciences or the arts).

Reviewed by Michelle Page, Assistant Professor, Worcester State University on 5/30/20

This text is exactly intended to be what it says: A quick guide. A basic outline of quantitative research processes, akin to cliff notes. The content provides only the essentials of a research process and contains key terms. A student or new... read more

This text is exactly intended to be what it says: A quick guide. A basic outline of quantitative research processes, akin to cliff notes. The content provides only the essentials of a research process and contains key terms. A student or new researcher would not be able to use this as a stand alone guide for quantitative pursuits without having a supplemental text that explains the steps in the process more comprehensively. The introduction does provide this caveat.

Content Accuracy rating: 3

There are no biases or errors that could be distinguished; however, it’s simplicity in content, although accurate for an outline of process, may lack a conveyance of the deeper meanings behind the specific processes explained about qualitative research.

The content is outlined in traditional format to highlight quantitative considerations for formatting research foundational pieces. The resources/references used to point the reader to literature sources can be easily updated with future editions.

The jargon in the text is simple to follow and provides adequate context for its purpose. It is simplified for its intention as a guide which is appropriate.

Each section of the text follows a consistent flow. Explanation of the research content or concept is defined and then a connection to literature is provided to expand the readers understanding of the section’s content. Terminology is consistent with the qualitative process.

As an “outline” and guide, this text can be used to quickly identify the critical parts of the quantitative process. Although each section does not provide deeper content for meaningful use as a stand alone text, it’s utility would be excellent as a reference for a course and can be used as an content guide for specific research courses.

The text’s outline and content are aligned and are in a logical flow in terms of the research considerations for quantitative research.

The only issue that the format was not able to provide was linkable articles. These would have to be cut and pasted into a browser. Functional clickable links in a text are very successful at leading the reader to the supplemental material.

No grammatical errors were noted.

This is a very good outline “guide” to help a new or student researcher to demystify the quantitative process. A successful outline of any process helps to guide work in a logical and systematic way. I think this simple guide is a great adjunct to more substantial research context.

Table of Contents

• Section 1: What will this resource do for you?
• Section 2: Why are you thinking about numbers? A discussion of the research question and paradigms.
• Section 3: An overview of the Research Process and Research Designs
• Section 4: Quantitative Research Methods
• Section 5: the data obtained from quantitative research
• Section 6: Analysis of data
• Section 7: Discussing your Results

Ancillary Material

About the book.

This resource is intended as an easy-to-use guide for anyone who needs some quick and simple advice on quantitative aspects of research in social sciences, covering subjects such as education, sociology, business, nursing. If you area qualitative researcher who needs to venture into the world of numbers, or a student instructed to undertake a quantitative research project despite a hatred for maths, then this booklet should be a real help.

The booklet was amended in 2022 to take into account previous review comments.  

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How to Write the Rationale of the Study in Research (Examples)

What is the Rationale of the Study?

The rationale of the study is the justification for taking on a given study. It explains the reason the study was conducted or should be conducted. This means the study rationale should explain to the reader or examiner why the study is/was necessary. It is also sometimes called the “purpose” or “justification” of a study. While this is not difficult to grasp in itself, you might wonder how the rationale of the study is different from your research question or from the statement of the problem of your study, and how it fits into the rest of your thesis or research paper. 

The rationale of the study links the background of the study to your specific research question and justifies the need for the latter on the basis of the former. In brief, you first provide and discuss existing data on the topic, and then you tell the reader, based on the background evidence you just presented, where you identified gaps or issues and why you think it is important to address those. The problem statement, lastly, is the formulation of the specific research question you choose to investigate, following logically from your rationale, and the approach you are planning to use to do that.

Table of Contents:

How to write a rationale for a research paper , how do you justify the need for a research study.

• Study Rationale Example: Where Does It Go In Your Paper?

The basis for writing a research rationale is preliminary data or a clear description of an observation. If you are doing basic/theoretical research, then a literature review will help you identify gaps in current knowledge. In applied/practical research, you base your rationale on an existing issue with a certain process (e.g., vaccine proof registration) or practice (e.g., patient treatment) that is well documented and needs to be addressed. By presenting the reader with earlier evidence or observations, you can (and have to) convince them that you are not just repeating what other people have already done or said and that your ideas are not coming out of thin air. 

Once you have explained where you are coming from, you should justify the need for doing additional research–this is essentially the rationale of your study. Finally, when you have convinced the reader of the purpose of your work, you can end your introduction section with the statement of the problem of your research that contains clear aims and objectives and also briefly describes (and justifies) your methodological approach. 

When is the Rationale for Research Written?

The author can present the study rationale both before and after the research is conducted. 

• Before conducting research : The study rationale is a central component of the research proposal . It represents the plan of your work, constructed before the study is actually executed.
• Once research has been conducted : After the study is completed, the rationale is presented in a research article or  PhD dissertation  to explain why you focused on this specific research question. When writing the study rationale for this purpose, the author should link the rationale of the research to the aims and outcomes of the study.

What to Include in the Study Rationale

Although every study rationale is different and discusses different specific elements of a study’s method or approach, there are some elements that should be included to write a good rationale. Make sure to touch on the following:

• A summary of conclusions from your review of the relevant literature
• What is currently unknown (gaps in knowledge)
• Inconclusive or contested results  from previous studies on the same or similar topic
• The necessity to improve or build on previous research, such as to improve methodology or utilize newer techniques and/or technologies

There are different types of limitations that you can use to justify the need for your study. In applied/practical research, the justification for investigating something is always that an existing process/practice has a problem or is not satisfactory. Let’s say, for example, that people in a certain country/city/community commonly complain about hospital care on weekends (not enough staff, not enough attention, no decisions being made), but you looked into it and realized that nobody ever investigated whether these perceived problems are actually based on objective shortages/non-availabilities of care or whether the lower numbers of patients who are treated during weekends are commensurate with the provided services.

In this case, “lack of data” is your justification for digging deeper into the problem. Or, if it is obvious that there is a shortage of staff and provided services on weekends, you could decide to investigate which of the usual procedures are skipped during weekends as a result and what the negative consequences are. 

In basic/theoretical research, lack of knowledge is of course a common and accepted justification for additional research—but make sure that it is not your only motivation. “Nobody has ever done this” is only a convincing reason for a study if you explain to the reader why you think we should know more about this specific phenomenon. If there is earlier research but you think it has limitations, then those can usually be classified into “methodological”, “contextual”, and “conceptual” limitations. To identify such limitations, you can ask specific questions and let those questions guide you when you explain to the reader why your study was necessary:

Methodological limitations

• Did earlier studies try but failed to measure/identify a specific phenomenon?
• Was earlier research based on incorrect conceptualizations of variables?
• Were earlier studies based on questionable operationalizations of key concepts?
• Did earlier studies use questionable or inappropriate research designs?

Contextual limitations

• Have recent changes in the studied problem made previous studies irrelevant?
• Are you studying a new/particular context that previous findings do not apply to?

Conceptual limitations

• Do previous findings only make sense within a specific framework or ideology?

Study Rationale Examples

Let’s look at an example from one of our earlier articles on the statement of the problem to clarify how your rationale fits into your introduction section. This is a very short introduction for a practical research study on the challenges of online learning. Your introduction might be much longer (especially the context/background section), and this example does not contain any sources (which you will have to provide for all claims you make and all earlier studies you cite)—but please pay attention to how the background presentation , rationale, and problem statement blend into each other in a logical way so that the reader can follow and has no reason to question your motivation or the foundation of your research.

Background presentation

Since the beginning of the Covid pandemic, most educational institutions around the world have transitioned to a fully online study model, at least during peak times of infections and social distancing measures. This transition has not been easy and even two years into the pandemic, problems with online teaching and studying persist (reference needed) . 

While the increasing gap between those with access to technology and equipment and those without access has been determined to be one of the main challenges (reference needed) , others claim that online learning offers more opportunities for many students by breaking down barriers of location and distance (reference needed) .  

Rationale of the study

Since teachers and students cannot wait for circumstances to go back to normal, the measures that schools and universities have implemented during the last two years, their advantages and disadvantages, and the impact of those measures on students’ progress, satisfaction, and well-being need to be understood so that improvements can be made and demographics that have been left behind can receive the support they need as soon as possible.

Statement of the problem

To identify what changes in the learning environment were considered the most challenging and how those changes relate to a variety of student outcome measures, we conducted surveys and interviews among teachers and students at ten institutions of higher education in four different major cities, two in the US (New York and Chicago), one in South Korea (Seoul), and one in the UK (London). Responses were analyzed with a focus on different student demographics and how they might have been affected differently by the current situation.

How long is a study rationale?

In a research article bound for journal publication, your rationale should not be longer than a few sentences (no longer than one brief paragraph). A  dissertation or thesis  usually allows for a longer description; depending on the length and nature of your document, this could be up to a couple of paragraphs in length. A completely novel or unconventional approach might warrant a longer and more detailed justification than an approach that slightly deviates from well-established methods and approaches.

Consider Using Professional Academic Editing Services

Now that you know how to write the rationale of the study for a research proposal or paper, you should make use of Wordvice AI’s free AI Grammar Checker , or receive professional academic proofreading services from Wordvice, including research paper editing services and manuscript editing services to polish your submitted research documents.

You can also find many more articles, for example on writing the other parts of your research paper , on choosing a title , or on making sure you understand and adhere to the author instructions before you submit to a journal, on the Wordvice academic resources pages.

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Exploring the influence of contextual factors and the caregiving process on caregiver burden and quality of life outcomes of heart failure (hf) dyads after a hospital discharge: a mixed-methods study.

1. Introduction

3.1. design, 3.2. ethics approval, 3.3. sample, 3.4. recruitment, enrollment, and data collection, 3.5. individual and family self-management theory (ifsmt), 3.6. dyad (patient and caregiver) measures, 3.7. measures for caregiver only, 3.8. measures for hf patient only, 3.9. data analysis, 4.1. demographic analysis, 4.1.1. social isolation.

The following quote from one of the caregivers illustrates COVID-19 social isolation: “ My social life has been a struggle with family and friends because of COVID. I want to keep him safe without the risk, so I keep company at a minimum and require everyone to wear a mask and be vaccinated to enter the house .”

4.1.2. Fear

“ His last hospital stay really scared me, and I was not really sure or got an answer about his prognosis .”

4.1.3. Patient Dependence upon Caregivers

An example quotes one of the caregivers: “ I am the only one that is even available. My first career was as a CNA, and I took care of his sister before she died. Both sets of parents passed away, and honestly, I am the only one. He would do it for me. We have been married for 25 years .”

4.1.4. Patient Expectations

An example quotes one of the caregivers: “ Yes. He defers to me even though he enjoys some of the tasks with other family or friends, such as going to Menards. I think he would enjoy these much better if he went with his friends or male relatives, but he wants me to go in case something happens .”

4.1.5. Financial Strain

A quote from one of the caregivers illustrates perceived financial strain and potential burden: “ Yes, she has not worked since 2016 and she has been in the hospital with so many visit(s) and now they have thrown around working her up for heart transplant and I am concerned with the cost of the surgery, hospital and post-transplant medications what our out of pocket and monthly costs are for us as I am the only income and we have a son still at home .”

5. Discussion

5.1. caregiving process, dyad congruence, 5.2. contextual factors, 5.3. limitations, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, appendix a. the zarit burden interview.

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Oliver, T.L.; Hetland, B.; Schmaderer, M.; Zolty, R.; Wichman, C.; Pozehl, B. Exploring the Influence of Contextual Factors and the Caregiving Process on Caregiver Burden and Quality of Life Outcomes of Heart Failure (HF) Dyads after a Hospital Discharge: A Mixed-Methods Study. J. Clin. Med. 2024 , 13 , 4797. https://doi.org/10.3390/jcm13164797

Oliver TL, Hetland B, Schmaderer M, Zolty R, Wichman C, Pozehl B. Exploring the Influence of Contextual Factors and the Caregiving Process on Caregiver Burden and Quality of Life Outcomes of Heart Failure (HF) Dyads after a Hospital Discharge: A Mixed-Methods Study. Journal of Clinical Medicine . 2024; 13(16):4797. https://doi.org/10.3390/jcm13164797

Oliver, Tamara L., Breanna Hetland, Myra Schmaderer, Ronald Zolty, Christopher Wichman, and Bunny Pozehl. 2024. "Exploring the Influence of Contextual Factors and the Caregiving Process on Caregiver Burden and Quality of Life Outcomes of Heart Failure (HF) Dyads after a Hospital Discharge: A Mixed-Methods Study" Journal of Clinical Medicine 13, no. 16: 4797. https://doi.org/10.3390/jcm13164797

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Doctoral Dissertations and Projects

Exploring the connection between student self-efficacy and student success in a prelicensure nursing programs: a predictive correlational study.

Leanne I. Moreira , Liberty University Follow

School of Education

Doctor of Philosophy

Jeffery Savage

HESI exit exam, GSE tool, prelicensure nursing students, self-efficacy

Disciplines

Recommended citation.

Moreira, Leanne I., "Exploring the Connection Between Student Self-Efficacy and Student Success in a Prelicensure Nursing Programs: A Predictive Correlational Study" (2024). Doctoral Dissertations and Projects . 5831. https://digitalcommons.liberty.edu/doctoral/5831

The purpose of this quantitative, nonexperimental, correlational study was to determine if a passing score on the standardized Health Education Systems Incorporated (HESI) exit examination for prelicensure nursing students can be predicted from the number of working hours and self-efficacy scores for those same students. This study is important because of high attrition and low retention rates among prelicensure nursing students, preventing more nurses from providing care to society at a time when the demand for nurses is high. The study included 79 prelicensure nursing students in rural Maine and Ohio. Two measurement instruments were included in this study: the general self-efficacy tool and the HESI exit exam. To collect data, Microsoft Forms and the Evolve.Elsevier HESI exit exam technologies were utilized. A logistic regression analysis evaluated the results. The results show no statistical significance or predictive relationship between the HESI exit exam, the number of hours prelicensure nursing students work, and self-efficacy, χ2(6) = 7.952, p = .242. Even though this study’s data did not result in statistical significance or predictive correlation, there is evidence to support how assessing student success through standardized testing could result in different data results compared to previous research. Recommendations for future research include increasing the number of participants to include more diversity, conducting qualitative research to gain a deeper understanding of what other barriers prelicensure nursing students endure outside of coursework, replicating this study with other variables found from previous research, and using an alternative self-efficacy tool specifically created for nursing students.

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Peer-reviewed

Research Article

Perspectives and challenges in developing and implementing integrated dengue surveillance tools and technology in Thailand: a qualitative study

Contributed equally to this work with: Chawarat Rotejanaprasert, Peerawich Armatrmontree

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

* E-mail: [email protected]

Affiliations Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

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

Affiliation Chulabhorn Learning and Research Centre, Chulabhorn Royal Academy, Bangkok, Thailand

Roles Conceptualization, Methodology, Validation, Writing – review & editing

Affiliation Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand

Roles Investigation, Validation, Writing – review & editing

Affiliations Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom, The Open University, Milton Keynes, United Kingdom

• Chawarat Rotejanaprasert, 
• Peerawich Armatrmontree, 
• Peerut Chienwichai, 
• Richard J. Maude

• Published: August 14, 2024
• https://doi.org/10.1371/journal.pntd.0012387
• Reader Comments

Dengue remains a persistent public health concern, especially in tropical and sub-tropical countries like Thailand. The development and utilization of quantitative tools and information technology show significant promise for enhancing public health policy decisions in integrated dengue control. However, the effective implementation of these tools faces multifaceted challenges and barriers that are relatively underexplored.

This qualitative study employed in-depth interviews to gain a better understanding of the experiences and challenges of quantitative tool development and implementation with key stakeholders involved in dengue control in Thailand, using a phenomenological framework. A diverse range of participants, including public health workers and dengue control experts, participated in these interviews. The collected interview data were systematically managed and investigated using thematic analysis to extract meaningful insights.

The ability to collect dengue surveillance data and conduct ongoing analyses were contingent upon the availability of individuals possessing essential digital literacy and analytical skills, which were often in short supply. Furthermore, effective space-time early warning and precise data collection were hindered by the absence of user-friendly tools, efficient reporting systems, and complexities in data integration. Additionally, the study underscored the importance of the crucial role of community involvement and collaboration among organizations involved in integrated dengue surveillance, control and quantitative tool development.

Conclusions

This study employed a qualitative approach to gain a deeper understanding of the contextual intricacies surrounding the development and implementation of quantitative tools, which, despite their potential for strengthening public health policy decisions in dengue control, remain relatively unexplored in the Thai context. The findings yield valuable insights and recommendations for the development and utilization of quantitative tools to support dengue control in Thailand. This information also has the potential to support use of such tools to exert impact beyond dengue to a broader spectrum of diseases.

Author summary

This study investigated the persistent public health challenge posed by dengue in tropical nations, with a specific focus on Thailand. Through qualitative research, it examined the potential of quantitative tools and information technology in integrated dengue control. Interviews with stakeholders, including public health workers and experts, revealed significant challenges. For instance, there was a shortage of essential skills for data collection and analysis, hampering effective surveillance and intervention. Additionally, issues such as the lack of user-friendly tools and complexities in data integration were identified. The study highlighted the importance of community involvement and collaboration among organizations. Recommendations included addressing these barriers by enhancing digital literacy and providing user-friendly tools. Overall, the study provided valuable insights into the development and utilization of quantitative tools, not only for dengue control but also for tackling a broader range of diseases.

Citation: Rotejanaprasert C, Armatrmontree P, Chienwichai P, Maude RJ (2024) Perspectives and challenges in developing and implementing integrated dengue surveillance tools and technology in Thailand: a qualitative study. PLoS Negl Trop Dis 18(8): e0012387. https://doi.org/10.1371/journal.pntd.0012387

Editor: Qu Cheng, Huazhong University of Science and Technology Tongji Medical College, CHINA

Received: January 23, 2024; Accepted: July 18, 2024; Published: August 14, 2024

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

Data Availability: The data underlying this article cannot be shared publicly due to the need to protect the confidentiality of the study participants. However, the anonymous data may be considered available upon reasonable request to the Ethics Committee of the Faculty of Tropical Medicine, Mahidol University at [email protected] .

Funding: This research was supported in whole, or in part, by the Faculty of Tropical Medicine, Mahidol University (CR), and the Wellcome Trust (CR and RJM) [Grant number 220211]. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

1. Introduction

Dengue is a major mosquito-borne disease, with an estimated global burden of 390 million annual infections, of which around 96 million present with clinical symptoms [ 1 ]. The virus is primarily transmitted by Aedes mosquitoes and is prevalent in tropical and sub-tropical regions. While vaccines have been developed, their efficacy is limited, necessitating pre-vaccination screening, and some have sparse safety data and are not widely accessible. Consequently, vector control remains the main focus of public health interventions to interrupt the infection cycle [ 2 ]. Timely and effective large-scale surveillance and interventions are needed to reduce the serious impacts of dengue epidemics on health, healthcare systems, and economies [ 3 , 4 ].

Dengue has a significant impact on public health, particularly in Southeast Asia, where Thailand has one of the highest burdens of infection worldwide [ 5 ]. With approximately 100,000 annual cases reported to the Thai Ministry of Public Health, it poses a substantial burden on the healthcare system and households [ 6 ]. Dengue is endemic in Thailand, leading to epidemics every few years, particularly during the rainy season from May to October [ 7 ]. These outbreaks strain public health infrastructure, emphasizing the need for timely surveillance and control measures [ 8 , 9 ]. Information technology and quantitative tools play a crucial role in formulating effective dengue prevention and surveillance plans in Thailand.

Information technology and quantitative tools are useful to inform public health policy decisions about dengue control [ 10 , 11 ]. Several models have been developed to understand the drivers of dengue transmission and apply them to disease surveillance and control efforts [ 8 , 12 – 15 ]. However, creating these tools is just the first step; their effective utilization is equally crucial. Without practical application, their potential remains untapped. Despite the numerous information technology and quantitative tools developed for dengue control, their adoption has been limited. Moreover, there has been insufficient understanding of the experiences, challenges, and barriers faced by stakeholders incorporated into the development of quantitative tools for them to empower policy formulation and enhance dengue control in the country.

To address this gap, qualitative research can be employed to explore the challenges and successes of quantitative tools in dengue control programs. For instance, in a qualitative study conducted in Bangkok, the challenges and successes of fumigation campaigns for dengue control were explored [ 6 ]. However, no qualitative study has been undertaken to tackle the gap between quantitative tool development and practical implementation in Thailand. Given the high dengue endemicity in Thailand, the need has intensified to unearth effective public health management strategies and approaches for controlling and preventing dengue epidemics. This requires addressing the gap in translating the development of quantitative tools into guiding the efficient use of the limited resources invested. Consequently, this study aimed to understand the challenges faced in the development and application of quantitative tools and information technology in dengue control activities within Thailand.

To achieve our objectives, we conducted a qualitative investigation aimed at comprehending the experiences, perspectives, and challenges associated with quantitative tools enhancing dengue control efforts across various administrative levels. To ensure a thorough understanding, we selected participants from four stakeholder groups: public health professionals, policymakers, researchers, and informaticians, chosen for their expertise and roles in dengue control. Additionally, we analyzed the essential components necessary for improving future quantitative tool development. The insights gained have the potential to guide the development and utilization of such tools, not only for dengue but also potentially for addressing related diseases or similar environments in other countries.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the Faculty of Tropical Medicine, Mahidol University. The submission number was TMEC 21–074 and the number of ethical approval certificate was MUTM 2021-071-01. Verbal consent was obtained and recorded in the audio files during the interview.

2.1. Study design and participants

This study employed a qualitative research design, chosen for its ability to explore deeply into the experiences of participants, surpassing the scope of quantitative methods [ 16 ]. Such research is typically conducted to investigate the meanings and interpretations held by individuals, providing a suitable approach to comprehending people’s underlying motivations [ 17 ], aligning well with the aims of our study. Given the impediments presented by the COVID-19 outbreak in Thailand during our study period, including travel and contact restrictions, we hence adopted the approach of conducting online in-depth semi-structured interviews. The phenomenological framework, a qualitative approach aiming to illuminate the essence of phenomena as experienced by individuals [ 18 , 19 ], was selected for this study to comprehensively understand the experiences, perspectives, and challenges associated with quantitative tools enhancing dengue control efforts across various administrative levels. This approach was chosen as it allows for an in-depth exploration of participants’ experiences, providing valuable insights into the complexities of utilizing quantitative tools in dengue control.

To capture a wide array of perspectives on these challenges, we engaged participants from four distinct stakeholder categories based on their roles and extensive experience in utilizing dengue control and surveillance quantitative tools in Thailand.

• Public health professionals (PH): This group, sourced from both provincial and national levels, actively engages in dengue control endeavors. Their roles included a spectrum of tasks, including mosquito spraying operations, executing public health initiatives, and coordinating community health activities.
• Policymakers (PM): This group represented the national dengue control program and local authorities within the Department of Disease Control, Ministry of Public Health, policymakers are instrumental in crafting dengue surveillance and control policies and guidelines. They oversaw the implementation of these measures by regional and local public health workers.
• Scientist or epidemiologist (SE): This was selected from epidemiologists and scientists with expertise in laboratory and population-based dengue research. Their responsibilities encompassed a broad spectrum of activities, ranging from conducting laboratory studies to investigating various facets of dengue transmission, entomology, fieldwork, pathogenesis, and control strategies.
• Informatician (IN): This group comprised programmers, analysts, engineers, and data experts, who have made significant contributions to dengue research and associated control activities. Their key responsibilities involved designing and implementing data collection systems, analyzing and interpreting data, and developing software tools to support dengue surveillance and control efforts.

The sample size for this study was determined through the application of theoretical saturation, a point reached when no further novel information is obtained from subsequent data collection [ 20 ]. Our pre-specified sample size calculation was informed by previous studies conducted in similar settings. For instance, a qualitative study on dengue control in Thailand involved face-to-face, in-depth interviews with 10 designated district officers in the Bangkok healthcare office, utilizing open-ended questions [ 6 ]. In another study, individual face-to-face interviews were conducted with healthcare personnel in Malaysia to gather their perspectives on the governance of dengue prevention and control with point of saturation observed after 19 interviews [ 21 ]. Similarly, a study examining the functioning of the Brazilian Dengue surveillance system obtained qualitative insights through interviews with 17 experts, focusing on data collection and reporting processes [ 22 ]. In light of these precedents, we pre-determined a sample size of approximately 16 in-depth interviews, a minimum of 4 participants per participant group, for our present study.

2.2. Data collection and analysis

Given the qualitative nature of our study, we employed a purposive sampling method to initially select participants for in-depth interviews in each stakeholder category. Our collaborators in the research community suggested the initial participants for scientists and informaticians, while the dengue national program recommended the initial public health personnel and policymakers for the interviews. Subsequently, we utilized a snowball sampling technique to expand our participant pool. Invitations to participate were extended via letters or email communications. Data collection occurred between November 2021 and October 2022.Prior to the interviews, participants received a written study overview and assurance of confidentiality. Verbal consent was obtained and recorded in the audio files during the interview. Demographic information was collected solely to characterize the interviewees, with no solicitation of identifiable data. The semi-structured interviews were conducted using a predefined question guide, focusing on key topics aligned with our study’s objectives. These interviews were audio-recorded and spanned in duration from approximately 30 to 60 minutes.

The interviews were transcribed verbatim from the audio recordings in their original language (Thai). Thematic analysis was conducted within a phenomenological framework [ 23 , 24 ]. A chronological review of the transcripts was undertaken to identify major themes, employing an inductive approach to data interpretation. Subsequently, the original data was coded and organized into sections with corresponding headings and subheadings [ 25 ]. Responses from multiple participants within each theme were consolidated, and in cases of theme inconsistencies, data was realigned into alternative themes until an appropriate structure was established. Any emerging themes that emerged during data collection and analysis were allocated additional headings and subheadings.

Manual coding was performed by PA. Codes that emerged from the initial translated interviews formed the basis of the codebook used to assess subsequent translated transcripts. The initial coding process was expanded into focused coding, where the association between different initial codes was explored based on frequency, sequence, correspondence, and similarity. CR independently repeated this process iteratively for all transcripts based on the codebook. Subsequently, CR and PA discussed the focused coding choices in detail. The final deductive codes were then grouped into meaningful categories, and sub-themes were generated by blending several categories together under the study objectives.

To ensure robustness, interviews and data collection transpired continuously throughout the period of subject enrollment. Data saturation, indicating the point at which no novel information was discerned from subsequent interviews [ 20 , 26 ], was evaluated. The study team made the decision on whether to continue additional interviews at this juncture. All qualitative data were managed using Microsoft Excel version 2108 and ATLAS.ti version 9. The research team held regular meetings and discussions to incorporate peer review, ensuring consistency and cross-checking the generated categories based on the study objectives. Additionally, team members collaboratively evaluated the findings and conclusions. To further minimize bias in data interpretation, the collected information was also shared with participants for their review. The final results were translated into English, with verbatim examples employed to illustrate key aspects of the themes. To protect the anonymity of our participants, pseudonyms were assigned to each participant category in relation to the quotations provided for each interviewee.

While our initial sample size determination aimed for 16 interviews, the diversity in experience among public health workers, influenced by their locations and duties, led us to recruit more participants than originally planned. We reached the point of saturation in this category after conducting 8 interviews, while for the other participant groups, we interviewed 4 participants each. Demographic information for the 20 total participants, including nine females and eleven males, is provided in Table 1 . The majority of participants held graduate degrees and had over five years of experience in dengue research and control activities.

Through analysis of the data obtained in our interviews, we identified several key conceptual themes. These include:

• Understanding the multifaceted dynamics of dengue transmission and control.
• Enhancing dengue surveillance through operational insights and technological innovations.
• Experiences and challenges in utilizing quantitative tools for dengue surveillance.
• Recommendations for developing quantitative tools and designing information technology for dengue control.
• Community participation and collaborative efforts in dengue surveillance and control.

Detailed results for each theme are presented below.

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https://doi.org/10.1371/journal.pntd.0012387.t001

Theme 1: Understanding the multifaceted dynamics of dengue transmission and control

The first theme derived from our interview data presents the intricate ecology of dengue transmission, shedding light on the multifaceted factors influencing the spread of the disease. While the primary focus of our study revolves around dengue surveillance, exploring this theme provides essential insights into the complex transmission pathways and associated risk factors identified during our interviews. By acknowledging the complexity of dengue transmission, we recognize its significance in informing the development of quantitative tools for surveillance. Our qualitative exploration with various stakeholders illuminates the diverse factors contributing to dengue transmission and control, which can serve as valuable inputs for the future development of surveillance technologies. Comparing these findings may aid in addressing gaps and enhancing the effectiveness of quantitative and information tools for dengue surveillance in diverse settings.

According to interviews with disease control professionals and dengue researchers, the spread of dengue was the result of multiple factors, including human carriers, vectors, and environmental elements. Improper storage of household items was highlighted as a key contributor to disease transmission, as these items could become breeding sites for mosquitoes. Preventive measures such as maintaining cleanliness in households and the proper disposal of containers were emphasized as effective strategies for controlling disease transmission.

Participants noted that controlling dengue was challenging due to the diverse factors contributing to its transmission, particularly environmental factors like weather patterns. Fluctuating weather patterns, particularly prolonged rainy seasons, significantly impacted mosquito breeding sites, elevating the risk of transmission. To address this challenge, they recommended proactive measures by governments and health authorities in affected communities. These measures included draining stagnant water, applying insecticides, and introducing mosquito repellent, which could disrupt the transmission cycle of dengue and protect public health.

SE: “…There are many complex factors that contribute to increasing dengue infection rates every year. There are many dimensions to this complexity, such as improper storage of household items that can become breeding sites for mosquitoes … Therefore, controlling dengue fever is very difficult and challenging…”

Throughout the interviews, participants also raised social aspects of dengue control. One critical issue was population migration, recognized as an important factor influencing disease spread. Understanding the behavioral patterns of migrant workers was deemed essential for effective dengue control. However, challenges emerged due to the transient nature of these individuals who often resided in rented accommodations near their workplaces, making it difficult to implement preventive measures.

PH: “…Population migration is also important, particularly in areas with high numbers of foreign workers. These workers often live in rented houses near factories and can be difficult to reach with disease prevention efforts… Environmental improvement can be challenging as they may not prioritize eliminating mosquito breeding grounds. This can lead to severe outbreaks. Understanding these factors is crucial in analyzing and planning disease control…”

The interviews mentioned about a noticeable pattern of dengue outbreaks initiating in urban centers or larger districts before spreading to rural areas. Popular tourist destinations were also susceptible to dengue outbreaks, highlighting the widespread impact of population movement on disease dynamics. Additionally, the timing of school calendars was identified as a significant factor affecting dengue cases. In Bangkok, for instance, the timing of school openings and closures influenced the incidence of dengue cases among students, showing the intricate interplay of social factors in dengue control.

Children and adolescents were identified as particularly vulnerable due to their frequent gatherings and close proximity to one another in schools, increasing the likelihood of transmission. Given the high risk among this group, maintaining cleanliness in schools and public areas, implementing proper hygiene practices, and promoting personal protection were emphasized as key strategies to reduce the risk of infection. Education and awareness programs conducted by the government and health authorities were recommended to promote proper hygiene practices among students and the general public.

PM: “…Population movement and density are probably very important because the pattern of dengue outbreaks that we see in a particular province usually starts in the cities or large districts before spreading to rural areas…”

PH: “…The opening and closing of schools in Bangkok has a clear impact on the number of dengue fever cases. During periods of school closures, such as during the 2020 outbreak, there was a significant decrease in the number of sick students…”

PH: “… Children are considered a high-risk group because they have to go to school, stay together, and there may be carriers in schools that make infection easier. Factors within the student or 5–14 year age group may be related to other external factors that affect behavior at this age…”

Theme 2: Enhancing dengue surveillance through operational insights and technological innovations

This theme explores the operational aspects and challenges encountered in dengue surveillance and control efforts, as revealed by stakeholders interviewed in our study. The interviews highlighted the pivotal role of Thailand’s disease surveillance system in monitoring and managing dengue outbreaks. The dengue control program’s prevention model introduced various measures, including mosquito control, waste management, and continuous public health initiatives, aimed at curtailing the disease’s spread. Collaboration between provincial public health departments, local health workers, and village volunteers was emphasized to ensure comprehensive mosquito control activities across various settings. Understanding these operational activities and challenges faced by public health professionals at different levels provides valuable insights for quantitative tool developers. By comprehending the experiences and perspectives of stakeholders involved in dengue surveillance, developers can tailor quantitative and information technologies to address specific needs and challenges in Thailand’s context.

One such example of software utilized for dengue operational activity is the TanRaBad software. Developed collaboratively by international organizations, it was designed for monitoring dengue outbreaks and gathering entomological index data. This includes conducting visual surveys of larval habitats as part of routine activities conducted by the Thai Department of Disease Control (DDC) [ 27 ]. The software enables prompt identification of any rise in vector density, with larval indices computed and utilized as parameters for vector control measures. To streamline the larval survey process, the DDC has implemented a mobile application called TanRabad-SURVEY, facilitating real-time data collection from larval surveys nationwide since 2016 [ 27 , 28 ]. The implementation of this application can be used during a survey, aligning with larval survey protocols established by the World Health Organization (WHO) and the DDC [ 27 , 29 ]. These technological innovations not only enhance the efficiency of dengue surveillance but also support decision-making processes for more effective vector control strategies.

However, according to insights gathered during the interviews, the effectiveness of the software was significantly influenced by the digital literacy of its users. Many individuals responsible for data collection, including village health volunteers and public health workers, encountered technological challenges. These individuals often belonged to an older demographic and had limited familiarity with digital tools, which impeded the software’s efficiency. Despite the well-conceived features of the software, its proper utilization remained imperative to ensure data accuracy for effective surveillance planning.

Given the challenges associated with collecting surveillance data, a critical aspect to achieve these goals involved conducting a rigorous analysis of disease trends and risk factors. This analysis formed the bedrock for shaping emergency response strategies. Public health professionals heavily relied on data, principles, and logical reasoning to scrutinize and control disease outbreaks. Therefore, the collection of precise and dependable data assumed a pivotal role, substantiating policymaking and catalyzing the realization of public health goals.

SE: “…When it comes to being an epidemiologist, we always rely on data, principles, and reasoning. If we have data, it can be beneficial for us and the community. One thing that epidemiologists do is to collect and store data, interpret data, and report on disease investigations. This is very important in policy making and achieving the goals that address the problems. Good data collection leads to good analysis and interpretation…”

During the study, early detection and notification, rapid implementation of disease control measures, and enhancing the readiness of healthcare personnel for disease management, in conjunction with the analysis of disease trends and risk factors, emerged as fundamental components for both emergency planning and response. The central government’s objective of reducing dengue incidence and mortality rates highlighted another significant challenge in dengue control—the precision and quality of data collected by local public health personnel. The iterative data collection processes often led to errors and inaccuracies, significantly impeding the effective formulation of disease control policies.

Furthermore, dengue control measures and resources are overseen and funded by a range of local organizations, which include not only governmental public health workers under the Ministry of Public Health but also local administrative bodies under other ministries. Collaborative efforts among these diverse organizations are vital for timely detection and intervention to halt disease transmission. However, the absence of harmonious collaboration among the different administrative levels responsible for dengue control and management poses a significant obstacle, leading to a fragmented and suboptimal approach to disease control.

PH: “…We receive policies from the central government, which they call the main goal of the country, that is, to reduce the number of patients, and the mortality rate is the indicator of performance…. The analysis of the situation shows that the area should be concerned and take some actions or conduct analysis to identify and address the risks or… However, it is difficult sometimes to collect data and they can be missing…”

PH: “…In terms of cooperation between organizations in disease control, I thought it might be problematic as the local health authority is primarily responsible for disease control. However, some work in public health may be carried out with other organizations, which may or may not be under their power. This could potentially lead to a lack of attention to public health problems if the local health administration does not have the necessary authority…”

Theme 3: Experiences and challenges in utilizing quantitative tools for dengue surveillance

In addition to operational challenges, our study uncovered experiences and significant issues related to the development and deployment of quantitative tools for dengue control. Discussions centered on resource allocation and disease control planning, particularly focusing on the creation of complex analytical models for forecasting future dengue case numbers. These sophisticated models often struggled with the demand for extensive and highly detailed data to ensure accuracy and effectiveness.

Furthermore, several data collection tools faced difficulties in achieving integration due to the complexity of consolidating information from diverse sources and organizations. This integration challenge resulted in a dearth of actionable insights, hindering the data collection process and impeding overall tool development. Analyzing dengue surveillance data, which is inherently intricate, requires the incorporation and integration of data from numerous sources and dimensions. In Thailand, these disparate data sources remain dispersed across various organizations, rendering their aggregation and comprehensive data analysis difficult. Furthermore, the development of quantitative tools and information technology encountered obstacles rooted in a misalignment with the actual needs and expectations of stakeholders.

Moreover, interviewees shed light on the challenges faced by village health volunteers, a critical user group responsible for monitoring and reporting dengue cases. Many of these volunteers encountered difficulties in understanding how to navigate the data collection tool, eroding their confidence in its effective use due to its complexity. Additionally, a subset of volunteers faced accessibility issues, as they lacked the financial means to acquire smartphones capable of running the application.

PM: “…Currently, the government is attempting to develop quantitative tools to control dengue. However, the obstacle is the lack of collaboration between organizations to integrate knowledge from experts in different fields across organizations. This means that the development of the tools cannot be implemented in real-life situations, and the available data cannot be utilized to its fullest potential…”

SE: “…The pandemic tracking app is a great system and idea. If we talk about the system, it is a great idea to have real-time monitoring for larvae surveys. However, there are limitations to its data collection due to the age of the community volunteers who use it. This is something they are currently trying to address…”

Interviewees also emphasized a critical challenge concerning the deficiency in data management tools and the requisite analytical skills, resulting in suboptimal data analytics. Although training programs had been developed, the practical application of these analytical skills had yet to reach a level of effective implementation. A concern that emerged during the interviews was the shortage of individuals proficient in computational programming languages such as R and Python. The utilization of data and the ability to conduct ongoing analyses were contingent upon the availability of individuals possessing these essential analytical skills, which were often in short supply. The absence of this foundational infrastructure posed a significant obstacle to the widespread adoption of technology and quantitative tools at different levels in decision-making processes. Moreover, financial constraints further complicated matters, as they hindered the integration of advanced tools and technologies, including artificial intelligence, despite a strong desire within the sector to leverage these capabilities.

Regarding disease control, the prevailing approach heavily relied on disease surveillance reports as the primary source of analysis. While surveillance and control mechanisms were in operation, the persistent issue of reporting delays and timeliness remained unresolved. Timeliness was a key component of effective surveillance, with local disease control authorities heavily dependent on timely reports to facilitate efficient disease control. Additionally, concerns regarding data coverage came to the fore, potentially impacting the overall efficacy of surveillance efforts. Although data management tools were employed to compile weekly disease situation reports, the data used for tracking and investigation were primarily drawn from the surveillance report. This also raised concerns about data coverage, particularly concerning private hospitals, which may not have been fully engaged in the reporting process.

PH: “…Currently, the public health sector lacks data management skills. While we use Excel at a certain level, proficiency in data analytics is still lacking. Though plans are underway to provide training, the reality is that people with skills in R and Python are still rare. While the surveillance data are manageable, there are still few individuals who can handle more complex datasets… There are budgetary limitations, and even if we want to use AI, there are still many constraints. Nevertheless, we are doing our best…”

SE: “…Dengue data is mostly based on disease reports to control the disease, and the analysis of high-risk areas. While there is already surveillance and control in place, the issue of reporting delays and timeliness still needs to be addressed…”

PH: “… Disease tracking and investigation are conducted by extracting data from the surveillance system, which contains information on patients receiving treatment in both public and private hospitals. However, the coverage of private hospitals may be incomplete depending on their willingness to participate…”

Theme 4: Recommendations for developing quantitative tools for dengue control

The interviews produced valuable recommendations with several pivotal considerations for the development of quantitative tools for dengue control activities. Foremost among these was the importance of involving experts from various relevant departments, fostering the integration of diverse knowledge and perspectives at the early stages. This interdisciplinary collaboration was deemed crucial for comprehensively designing effective tools to address the multifaceted challenges posed by dengue. Additionally, the input from end-users emerged as a critical factor in the tool development process. This user-centric approach was seen as fundamental for enhancing the practical utility of these tools in real-world dengue control efforts.

IN: “…In reality, technology has the potential to solve the problem of dengue and control its spread at all levels, from national to community. However, the challenge lies in whether technology will be suitable to address the issue or not. Therefore, it is crucial to foster collaboration between organizations to tackle the problem effectively…”

Furthermore, the interviews emphasized the need to address both spatial and temporal dimensions in dengue control planning. Such tools would play a crucial role in promptly predicting dengue incidence outbreaks, accommodating reporting delays, and offering a comprehensive overview of the disease landscape from the national level down to local granularity. Spatial identification would provide precise coordinates, facilitating targeted mosquito elimination efforts, ensuring not only timely but highly accurate interventions. To make these critical insights readily accessible and usable, participants proposed creating a user-friendly interface or dashboard. This platform would serve as an information hub, encompassing space-time disease dynamics, enabling comprehensive and precise situation assessments and preparedness evaluations across different locations.

Among these recommendations, early warning with spatial identification emerged as an important strategy in dengue control. This approach gained particular importance due to the nationwide prevalence of the disease and resource constraints. Analyzing the disease landscape and identifying hotspots with the highest caseloads would enable targeted interventions. Knowing where to deploy additional vector control measures such as insecticide fogging and breeding site reduction, and diagnostic testing kits to achieve maximum impact all relied on accurate predictions of expected case estimates. Therefore, the development and deployment of quantitative tools hold promise for facilitating resource allocation and strengthening the effective response to the fluctuating dengue threat.

PH: “…Spatial identification will help control dengue more efficiently because the disease is widespread throughout the country. Due to resource constraints and budget limitations, intensive operations may not be possible in every area… By using timely analysis of the situation and identifying high risk areas, we can focus our efforts on those areas to prevent spread to neighboring areas…”

PM: “…Controlling dengue fever using quantitative tools like IT modeling is very useful because the disease has a clear seasonal pattern. We know when it will spread, but we don’t know how severe the outbreak will be each year. This makes it difficult to prepare resources not just in the public health sector but also in the local community…”

The interviews underscored the importance of user-friendly data tools not only for macro-level dengue control planning but also at the local level, considering variations in technology skills among local public health officers. Recommendations included the development of mosquito surveillance devices capable of autonomously alerting residents in affected areas to reduce reliance on village health volunteers with varying skills. Participants also stressed the need to collect data in an easily adaptable format for non-technical personnel. This approach could enhance data coverage, provided more user-friendly tools become available.

In pursuit of sustainable solutions for data integration and computational modeling development, participants proposed the idea of making all satellite and remote sensing data openly accessible. This approach would democratize data access for stakeholders and researchers, promoting more effective collaboration and research initiatives to enhance surveillance tools. However, implementing this transition would necessitate a shift in how Thai organizations handle data, particularly those with ties to foreign agencies, to embrace the concept of free data access. Despite the challenges, this move towards integration and inter-organizational collaboration was considered essential for creating practical, real-time tools and improving dengue surveillance and response in Thailand.

IN: “… Data should be stored in a database format, but non-data science personnel tend to summarize the data they collect, which makes it difficult to use the information… However, with the availability of free software, this issue has improved significantly. The sustainable solution is to access data by fixing the system, such as releasing data on a free access cloud, which would require organizations in Thailand to adapt to a new system…”

Theme 5: Community participation and collaborative efforts in dengue surveillance and control

In addition to discussing dengue surveillance and quantitative tool development, interview participants highlighted the significance of community participation and collaborative efforts in controlling dengue. They identified a challenge in dengue control related to the attitudes and behaviors of individuals and the community’s willingness to adopt disease control measures. Participants emphasized that successful dengue quantitative tool development and solutions require collaboration among agencies involved in local community, vector control, and environmental efforts. Despite the availability of resources, motivating individuals to proactively engage in control measures can be challenging. Therefore, community involvement was recognized as an essential component of effective disease control. To enhance community involvement, participants stressed the importance of raising public awareness about the severity of the disease and emphasizing the need for prompt preventive actions.

Community engagement is crucial to strengthen disease control efforts. Dengue is not just a governmental responsibility but it has become a shared public and community concern. Consequently, community participation is essential in disease control. The effective prevention of disease spread needs a transformation in people’s behaviors and attitudes, involving the significance of awareness campaigns, educational outreach, and community engagement. Thus, supported by information from the interviews, it is important to incorporate local contextual factors into the development of quantitative tools and modeling for dengue surveillance.

SE: “…Even with innovative solutions and comprehensive databases, controlling the spread of dengue fever is not possible without active community participation. In cities, mosquitoes are in close proximity to people, making it challenging to combat the disease. Dengue is highly contagious and the vector is extremely resilient, able to survive in dry conditions with eggs that can last up to a year. Its unique biology enables it to maintain infection, making it difficult to eradicate. Thus, the most effective solution is control, and community personnel play a critical role…”

Beyond the community, effective management and the development of tools for dengue surveillance rely on collaboration among organizations in both the public and private sectors. Sharing data is a critical component of surveillance and information systems. Without this collaboration, effective surveillance becomes more challenging. In addition, this requires not only government funding and infrastructure support for dengue prevention but also active participation from community organizations and individuals. These contributions should also be directed toward localized activities aimed at addressing specific challenges in each area. While local public health personnel play a crucial role, participants acknowledged that relying solely on them for disease control is insufficient. Dengue control is a multifaceted challenge, encompassing both public health and environmental management. Therefore, collaborative efforts are essential, as a comprehensive approach is required to achieve dengue control goals.

PH: “…The biggest challenge in disease control is community involvement, which can be divided into three things: people, money, and management. Money and resources can be obtained, but managing people to behave as desired is difficult. We need good behavior, environmental improvements, and mosquito control, which are difficult because the public do not play their part…”

PH: “…If village health volunteers were required to do this in every village, it would not work. If the problem is not addressed properly from the beginning, it cannot be successfully resolved. Do you think it is an environmental problem or a people problem?…”

This study aimed to gain a deeper understanding of the experiences and challenges related to the development and application of quantitative tools in dengue control programs in Thailand. During the study, various aspects of surveillance activities and the use of quantitative tools in dengue control in Thailand were explored.

Complexity of dengue transmission and vector surveillance

The study revealed the intricate dynamics of dengue ecology and its transmission pathways. Participants underscored a multitude of factors fueling dengue spread, notably the absence of specific treatments for dengue fever and the limited efficacy of existing vaccines. In light of these challenges, vector surveillance and management emerged as pivotal strategies for dengue prevention and control [ 30 ]. However, it was noted that traditional larval mosquito index monitoring may not consistently address dengue risk. Surveillance methods focusing on pupal and adult mosquito stages could offer more accurate estimates of dengue transmission risk, although implementation poses challenges [ 31 ]. Thus, the understanding of these multifaceted factors underscores the importance of comprehensive data collection, particularly in the context of vector control initiatives. Furthermore, the insights gleaned from this study regarding the complexity of dengue transmission hold significant implications for the future development of quantitative tools for dengue surveillance.

Engaging communities for effective dengue control

Community participation emerged as a crucial aspect of dengue control efforts in our study, encompassing elements such as health literacy, self-protection practices, and proper household item storage. These factors were identified as significant contributors to disease transmission. This finding resonates with existing research, which has demonstrated that successful dengue vector control initiatives rely heavily on active community involvement [ 32 , 33 ]. Moreover, studies have identified common barriers to community engagement, including low awareness levels and a lack of government commitment and financial support, as observed in regions such as Vietnam and Cuba [ 34 , 35 ]. These examples underscore the complex interplay of factors influencing community participation in dengue control and highlight the necessity of community-driven approaches. Such approaches are pertinent not only to Thailand but also to other regions facing similar challenges. The effectiveness of dengue surveillance and the development of quantitative tools may be hindered by individual attitudes and community engagement barriers. Therefore, integrating local community factors into tool development and modeling processes can enhance the effectiveness of dengue surveillance strategies.

Prioritizing user-centric approaches in quantitative tool development

This study identified a significant challenge related to the development of dengue surveillance tools, wherein these tools often prove impractical and fail to adequately address stakeholder requirements. Similar challenges have been observed in the development of healthcare tools and system processes, where stakeholders are frequently overlooked during the design phase. This oversight leads to the creation of products that remain underutilized, as they neglect the user’s context, needs, and inherent vulnerabilities within these systems [ 36 , 37 ].

To overcome this challenge, the adoption of user-centric methodologies, such as Design Thinking, is essential. These methodologies guide investigators in incorporating user needs and feedback throughout the development process [ 38 , 39 ]. Research has demonstrated the benefits of stakeholder involvement in addressing critical challenges within national health information systems, as demonstrated during the 2014 Ebola outbreak [ 40 ]. Closing the gap between research production and its real-world application remains a significant challenge for the health research system [ 41 ]. By involving stakeholders in the development of quantitative tools, their practicality and effectiveness are enhanced, thereby contributing to bridging this gap.

Incorporating spatial and temporal dimensions in dengue modeling and control

While numerous dengue models employing various methods were proposed [ 42 ], some studies reported inaccuracies in dengue case predictions. These inaccuracies were attributed to the geo-spatial variations in climate and environment within regions [ 43 ]. Our study echoed similar findings, emphasizing the importance of developments that considered both spatial and temporal dimensions to effectively control dengue transmission. This emphasis aligned not only with research in Thailand but also in other regions [ 44 , 45 ]. Furthermore, the significance of addressing reporting delays and ensuring timely responses in controlling the spread of dengue transmission was underscored. This aligned with other modeling research conducted in Thailand [ 8 , 46 , 47 ]. Delays in reporting dengue cases frequently impeded timely interventions, highlighting the necessity of a system that ensured prompt reporting for early outbreak detection and more efficient resource management [ 48 ].

Enhancing technology literacy and accessibility for dengue control tools

The study highlighted the significance of technology literacy and accessibility in the development of tools to support dengue control. Participants emphasized the importance of user-friendliness and effective data management to address these issues. Recognizing the technical limitations faced by many local public health workers, ensuring technological accessibility is pivotal for enhancing the usability of the developed quantitative tools. These findings align with previous research, which identified technology literacy as a potential barrier to implementation for health [ 49 ]. Additionally, other studies have indicated the positive impact of user interface design in health information systems on health worker performance [ 50 , 51 ].

Study limitations

While our study findings offer valuable guidance for future tool development, it is important to acknowledge certain study limitations. Due to COVID-19 restrictions, our qualitative approach was limited to online in-depth interviews during the study period. From our experience in this study, we recognized that online interviews require more than facilitating the content and flow of the discussion. Engaging in online interactions on a research topic, particularly with unfamiliar individuals, proved mentally demanding. Additionally, we encountered occasional technical issues that caused lags in conversations during some interviews. However, we managed this challenge by adjusting the pace of the conversation with slightly longer pauses between sentences or questions, which helped maintain momentum. Nonetheless, online interviews enabled us to reach a wider range of participants, as the location of the research team no longer limited the geographic parameters of the study population. Online platforms have the potential to eliminate geographic barriers and may prompt researchers to approach their research questions differently. While online methods allow for broader sampling and recruitment, researchers should remain mindful of methodological concerns.

Due to purposive and snowball sampling, we recognize that the results may not comprehensively represent the views of the entire population [ 52 , 53 ]. Nevertheless, it is crucial to emphasize that our study provides an invaluable and contextually-rich understanding of the meanings and experiences associated with the development and implementation of quantitative tools and information technology for dengue surveillance in the Thai context. This nuanced insight holds significant value for future development efforts, particularly when addressing issues in Thailand that have been relatively less explored. It is also essential to exercise caution when attempting to apply these findings to other diseases or countries which have different surveillance systems, sets of interventions, and contributing factors, introducing uncertainty regarding the generalizability of our conclusions. Nonetheless, certain aspects of our findings may offer valuable insights, particularly for mosquito-borne diseases, with potentially broader applications.

Dengue remains a significant public health challenge in tropical and sub-tropical regions, particularly in Thailand. While the potential of quantitative tools to inform and enhance public health policy decisions for dengue control is evident, the path to effective implementation is riddled with numerous challenges and barriers. This study has illuminated essential components crucial for strengthening the effectiveness of future quantitative tool development in the domain of dengue surveillance in Thailand. Key dimensions highlighted in our research include the importance of stakeholder engagement, capacity building, and the establishment of more robust data collection and sharing mechanisms. By addressing these factors, we can enhance the utility and impact of quantitative tools in supporting dengue prevention and control strategies.

Looking ahead, future research efforts could explore innovative approaches to overcome the challenges identified in this study. This could involve further investigation into user-centered design methodologies and the development of tailored interventions to address specific needs and barriers encountered by stakeholders. Moreover, increasing technological accessibility by ensuring new tools are user-friendly and providing necessary support and resources to all users, regardless of their technical proficiency, is essential. Additionally, investing in capacity building by offering training and resources to local health workers and organizations is crucial for effectively using and maintaining new surveillance technologies, ensuring long-term sustainability. he findings of this study have broader implications beyond Thailand, extending to other regions facing similar challenges in dengue surveillance and control efforts. By sharing our insights, we hope to contribute to the ongoing global efforts to combat vector-borne diseases and advance public health initiatives worldwide.

Acknowledgments

This research would not have been possible without all stakeholders for their assistance throughout the development of this study. In particular, we would like to express our gratitude to Dr Darin Areechokchai for her support and advice and Professor Andrew B. Lawson for inspiring this research.

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• Published: 16 August 2024

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

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

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

Metrics details

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

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

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

Conclusions

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

Peer Review reports

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

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

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

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

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

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

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

Study setting and design

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

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

Quantitative phase

Population and sampling.

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

Data collection

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

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

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

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

Data analysis

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

Qualitative phase

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

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

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

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

Perceptions of students of their learning environment

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

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

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

Students’ perceptions of their professional identities

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

Association between MCPIS-9 and DREEM

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

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

Theme 1. The personal component of the learning environment

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

Sub-theme 1.1. Experiences influencing professional identity formation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Subtheme 1.3. Students’ mental and physical well-being

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

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

Another student added:

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

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

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

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

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

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

Theme 2. Social component of the learning environment

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

Sub-theme 2.1. Opportunities for community engagement

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

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

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

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

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

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

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

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

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

Theme 3. Organizational component of the learning environment

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

Sub-theme 3.1. Curriculum and study plan

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

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

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

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

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

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

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

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

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

Sub-theme 3.2. Organizational climate and policies

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

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

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

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

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

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

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

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

Theme 4. Materialistic component of the learning environment

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

Sub-theme 4.1. The physical space for learning

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

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

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

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

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

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

Sub-theme 4.2. The virtual space for online learning

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

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

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

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

Theme 5. Characteristics of an ideal learning environment

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

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

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

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

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

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

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

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

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

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

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

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

Subtheme 5.2. Supportive physical environment

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

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

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

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

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

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

Data integration

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

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

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

Perceptions of the learning environment

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

Association between learning environment and professional identity

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

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

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

Expectations of the ideal educational learning environment

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

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

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

Application of findings

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

Limitations

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

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

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

Availability of data and materials

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

Abbreviations

United Nations Educational, Scientific, and Cultural Organization

European Union

American Council on Education

World Federation for Medical Education

Communities of Practice

Qatar University Health

College of Health Sciences

College of Pharmacy

College of Medicine

Dental Medicine

College of Nursing

Human Nutrition

Biomedical Science

Public Health

Physiotherapy

Dundee Ready Education Environment Measure

Perception to Learning

Perception to Teachers

Academic Self-Perception

Perception of the Atmosphere

Social Self-Perception

Macleod Clark Professional Identity Scale

Focus Group

InterProfessional Education

Project-Based Learning

Hamad Medical Corporation

Hamad Bin Khalifa Medical City

Artificial Intelligence

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Acknowledgements

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

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

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Study conception and design: BM, and SE; data collection: BM, OY, AA, and AD; analysis and interpretation of results: all authors; draft manuscript preparation: all authors. All authors reviewed the results and approved the final version of the manuscript.

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

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Person-centred quality indicators for Australian aged care assessment services: a mixed methods study

• Sandra Smith 1 ,
• Catherine Travers 2 ,
• Melinda Martin-Khan 1 , 5 ,
• Ivy Webb 1 , 6 ,
• Elizabeth Miller 1 , 6 ,
• Jane Thompson 1 , 6 &
• Natasha Roberts 3 , 4  

Research Involvement and Engagement volume  10 , Article number:  88 ( 2024 ) Cite this article

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Metrics details

Aged Care Assessment Teams are the assessment component of the Australian aged care system. Their purpose is to undertake needs-based assessments to determine an older person’s eligibility for, and access to Commonwealth-funded aged care services. There are no measures that tell us if the aged care assessment service is of high quality from the perspective of the person being assessed. Quality measures have been developed and introduced in Australian residential aged care facilities. These however, have not considered the perspectives of those living in this setting. Quality measures for home care services have also been recommended.

This research aims to address the gap in person-centred quality measures by asking current and future service users of aged care assessment services to vote on the importance of 24 person-centred quality indicators (PC-QIs), that were developed in a previous study using a modified Delphi method approach supported by engagement with a consumer led Advisory Board.

This mixed methods study used the RAND/UCLA Appropriateness Method to reach consensus on a final set of PC-QIs. Twenty-five community-dwelling older people in Brisbane, Australia, voted on the importance of 24 PC-QIs using a five-point Likert scale. A consensus statement for PC-QI elimination was determined prior to participants voting. Voting was undertaken with participants individually either face-to-face or via telephone, in their homes. To capture any narrative provided by participants regarding each PC-QI, participant voting sessions were audio-recorded and subsequently transcribed verbatim.

Quantitative data from participant votes for each PC-QI were calculated and statistically described by median, interquartile range, consensus met, percentile, percentile rank, rank order, median and standard deviation. PC-QIs were then assessed against the consensus statement for elimination and rank ordered according to importance to participants. Content analysis of qualitative data from audio transcriptions was conducted to determine the presence of certain words supporting participant votes for each PC-QI.

No PC-QIs were eliminated during voting. Variation existed among participants’ ratings of importance for each PC-QI. Final quality domains, their respective title, quality indicator descriptor and supporting qualitative data are presented. Five PC-QIs had a median of five, no votes recorded below four, an interquartile range of zero, and a rank order score of one, two and four, out of a possible ten, indicating they were of highest importance to participants.

Participants reached consensus on 24 evidence-based PC-QIs that represent measures of quality of aged care assessment services from the perspectives of current and future service users.

Plain English Summary

In Australia, people 65 years or over, and Aboriginal and Torres Strait Islander Peoples 50 years or over, can apply to access aged care services funded by the Australian government (Australian Government, Department of Health and Aged Care. 2021-2022 Report on the Operation of the Aged Care Act 1997, 2022). Services the government funds include supporting older people to live at home, residential aged care if the person can’t live at home, and short-term rehabilitation (Australian Government, Department of Health and Aged Care. 2021-2022 Report on the Operation of the Aged Care Act 1997, 2022). To access these services, a person needs to fill out an application form and undergo an aged care assessment. Another person can help complete the application. The purpose of the aged care assessment is to assess the person’s needs to determine what aged care services they are eligible to access. There are no measures that tell us if the aged care assessment service is of high quality from the perspective of the person being assessed. Twenty-four measures of quality were developed in a previous study with support from a consumer Advisory Board (Smith S, Travers C, Roberts N, Martin‐Khan M. Health Expect, 2024). This study asked 25 older people living in Brisbane, the importance of the 24 measures, to decide if any should be eliminated. People were asked to vote using a number scale where number one meant it ‘wasn’t important’, and five meant it was ‘extremely important.’ A consensus statement was agreed to decide if a measure would be eliminated. All measures were voted as being important with no measure eliminated. Quality Measures voted as being important included receiving assessments from knowledgeable health care staff, who treated them with dignity and respect, adopted a person-centred approach, established a collaborative relationship, and communicated clearly.

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Introduction

In June 2022, older people (people aged 65 years or greater, 50 years or greater for Indigenous Australians, referred to as ‘older people’ or ‘older Australians’ in this paper) comprised approximately 16.5% (4.4 million) of the total Australian population [ 1 ]. By 2032 it is projected that this group will increase to 18.3% (5.6 million) [ 1 ]. During 2021–22, Aged Care Assessment Teams (ACATs) across Australia conducted 200,562 assessments to determine an older person’s eligibility for government-funded aged care services [ 2 ]. The Australian aged care system funds residential aged care, transition care, home care, short term restorative care and home support services to eligible older Australians [ 2 ]. To be eligible, a person must apply for care in accordance with the Aged Care Act 1997 [ 3 ] , and have their care needs assessed by an aged care assessor [ 4 ]. Most developed countries including England, Scotland, United States, Canada, Singapore, Japan, Sweden, Finland, France, Denmark, the Netherlands and Germany have a process which enables the assessment of an older person to determine their care needs and appropriate delivery of social and/or long-term care [ 5 ].

In Australia, care needs (including social, physical, medical, psychological and home and personal safety) are determined by the National Screening and Assessment Framework [ 6 ], conducted by ACATs, of which there are 80 nationally across all states and territories. The Australian government provides funding to state and territory governments to operationally administer ACATs [ 7 ], in which aged care assessors deliver assessment services on behalf of the Federal Minister for Health and Aged Care and the Australian Government Department of Health and Aged Care [ 8 ]. Operational responsibilities of states and territories include managing the timely delivery of assessments under the Aged Care Act 1997 , and the management, training and performance of individual aged care assessors [ 7 ]. In essence, the pathway to government-funded aged care services in Australia begins with an older person applying for care, followed by a needs-based assessment conducted by ACATs to determine their eligibility for services. Once eligibility has been determined and access granted, government-funded aged care services are delivered by approved aged care service providers under the Aged Care Act 1997 . The functions of ACATs and approved aged care service providers are separate, such that, ACATs determine eligibility for, and access to services, while approved aged care service providers deliver care and support services.

While the operation of ACATs is managed by states and territories, the Commonwealth Department of Health and Aged Care has oversight and responsibility for the Aged Care Assessment Program, within which ACATs sit, including monitoring and reporting of assessment service performance against key performance indicators [ 9 ]. The quality of assessment services is governed by the Department of Health and Aged Care according to the Aged Care Assessment Quality Framework [ 10 ], using a three-tiered approach (Table  1 ).

The quality of residential aged care facilities (RACF) is also governed by the Department of Health and Aged Care, using the National Aged Care Quality Indicator Program. This program is mandatory and requires RACFs to collect and report on quality indicators (QIs) across the areas of pressure injuries, physical restraint, unplanned weight loss, falls and major injury, medication management, activities of daily living, incontinence care, hospitalisation, workforce, consumer experience and quality of life [ 11 ]. QIs for home care services were recommended by the Royal Commission into Aged Care Quality and Safety [ 12 ], and while this recommendation is viewed as a positive step toward measuring system performance, it fails to recognise the essential functions of ACATs which initially assess an older person’s eligibility to access government-funded aged care services. An older person’s journey through the Australian aged care system is not always linear. They may move between aged care types, and subsequently may need to undergo more than one assessment to determine their eligibility (for example when transitioning from hospital to home and requiring short term rehabilitation care or transitioning from the community to a residential aged care facility). In essence, to access any aged care type an assessment to determine eligibility is required in the first instance.

QIs can be used in various ways including documenting quality of care; making comparisons across time; setting priorities; supporting accountability; regulation and accreditation; and supporting quality improvement and a person’s choice of providers [ 13 ]. QIs can relate to the structure, process or outcomes of health care. Structure refers to attributes of the setting in which the care occurs, process refers to what is being done in the giving and receiving of care and outcomes describe the effects of care on the recipient’s health status [ 14 ]. PC-QIs have been defined as “the unit of measurement of the healthcare system, organisational, or individual performance, that quantifies patients’ and families’ experiences with the care received and the experience of an individual who needs to contact healthcare services”, a definition the Agency for Healthcare Research and Quality adapted to reflect the focus on the person and family [[ 15 ] (p.2)]. Whilst PC-QIs can be in any of these three formats, they are most commonly either process or outcome QIs.

The Australian government has committed to the provision of high-quality care for older Australians and as part of reforming the aged care system, it is developing an end-to-end system where the person drawing on the service drives quality [ 10 ]. For this to happen, it seems fitting that PC-QIs are used to measure system performance.

Twenty-four process PC-QIs measuring the quality of the aged care assessment service were developed in a previous study using a modified Delphi method [ 16 ]. This work was undertaken in collaboration with the evaluating Quality of Care (eQC) Patient and Carer Advisory Board, who were instrumental in assessing the person-centredness of the PC-QIs. The Board is a collaboration of patients and carers who support the embedding of partnerships between lived experience experts and researchers undertaking quality-of-care research. Members of the eQC Board were engaged in this study to provide their reflections on the research findings regarding the importance of PC-QIs for aged care assessment services, for current and future service users (Table  2 ).

This paper presents the theoretical assessment confirming the consensus of PC-QIs for aged care assessment services. The objective of this study was to investigate the degree to which the PC-QIs reflected current and future service users’ perspectives of important quality measures using the RAND-UCLA Appropriateness Method to reach consensus, to reflect a person-centred approach. The Guidance for Reporting Involvement of Patients and the Public (revised) [ 17 ] (GRIPP2) short form (Appendix 1) outlines our reporting of public involvement in this study.

This was a mixed methods study of 25 individual voting sessions conducted face-to-face or via telephone, depending on the participants preference. During the session, participants voted on the importance of each PC-QI using a five-point rating scale ranging [ 18 ] from 1 ‘extremely unimportant’ to 5 ‘extremely important.’ At the commencement of the session participants were advised they could provide verbal comments throughout. Sessions were audio recorded. Quantitative data from participant votes for each PC-QI were calculated and statistically described by median, interquartile range, consensus met, percentile, percentile rank, rank order, median and standard deviation. Quantitative data for each PC-QI were then assessed against the consensus statement for elimination and rank ordered according to importance to participants. Content analysis of qualitative data from audio transcriptions was conducted to determine the presence of certain words supporting participant votes for each PC-QI.

This study was conducted in the homes of community-dwelling older people residing in the greater Brisbane area, Australia. Ranked one of the third largest metropolitan areas in Australia, the estimated resident population of Brisbane was 2.5 million in June 2022 and one of the fastest growing metropolitan areas in Australia [ 19 ].

Ethical approval

This study was approved by the Royal Brisbane & Women’s Hospital Human Research and Ethics Committee (HREC/2022/QRBW/82417) and ratified by The University of Queensland research ethics and integrity unit (2022/HE001183). Governance approval was granted from Metro North Hospital and Health Service Community & Oral Health Directorate, Queensland.

Recruitment of participants

A suggested representative sample of older people was determined using data from the 2020–2021 report on the Aged Care Act 1997 [ 2 ] and the Australian Institute of Health and Welfare Dementia in Australia 2021 report [ 20 ]. Participants were recruited by advertising the study using several strategies including word-of-mouth, information flyers, social media, public speaking engagements and carer support networks. Information flyers were included in appointment paperwork mailed out to prospective clients of the largest aged care assessment service in Queensland, and a community-based approach was adopted to facilitate greater participation in, and acceptance of the study by engaging with community groups and leaders of current and future aged care assessment service users [ 21 ]. Meetings were held with presidents and newsletter editors of six older peoples’ community groups, to provide an overview of the research study, enable opportunities to ask questions, and identify a suitable approach for advertising the study more broadly to members of each group. A presentation was delivered to members of Shed West Community Men’s Shed at their monthly meeting, where information about the research study was provided and an open forum was held to discuss pathways to access aged care services in Australia, including the role of aged care assessment services.

Information about the study was advertised in newsletters of relevant older people’s groups (e.g., Probus Club, Care of the Older Australian Queensland Branch, National Seniors Kenmore, and Shed West).

Facebook was the primary social media platform used to advertise the study. Twenty-eight people contacted the lead investigator, and 25 confirmed their interest to participate. A participant information sheet and consent form, which comprised of an accompanying easy read version (Appendix 2), an outline of the service elements of an aged care assessment (Appendix 3) and information on how an aged care assessment can help (Appendix 4), were mailed to interested participants.

Participant voting sessions

Participant sessions were undertaken from February to July 2023. Voting sessions ranged from 60 to 90 min in duration. The capacity of each participant to consent to take part in the research was confirmed at the time of the session using the Evaluation to Sign Consent Measure. This tool assesses a person’s cognitive capacity to understand and consent to participate in research and has been validated for use with people living in residential aged care [ 22 ]. The tool can be tailored to a research protocol and asks participants to respond to five questions [ 23 ]. Questions asked of participants and the corresponding acceptable responses are detailed in Table  3 .

Participants were advised they could withdraw from the study at any time. Demographic data were collected from participants before the session commenced. A paper-based recording sheet outlining the 24 PC-QIs was provided to participants (Appendix 5), who were then asked to record their vote for each PC-QI on the sheet provided. Before voting commenced, participants were provided background information about the study and an opportunity to review the two information sheets that were previously sent in the mail (Appendices 3 & 4). Consent to record the session was provided verbally by participants. Participant voting sheets were collated after the session and the audio recordings of the sessions were subsequently transcribed verbatim.

Data collection

Participants completed a paper-based data sheet with demographic data including their gender, age, ethnicity, previous involvement with an aged care assessment service, and primary medical diagnosis, including diagnosis of dementia and/or difficulties with memory. The quantitative data from participants’ voting sheets and the qualitative data from the audiotaped recordings of the sessions comprised the preliminary data for analysis. Following the session, participants were asked whether they would like to check their responses by subsequently reviewing their transcript (when available) and voting record sheet via email, hard copy in the mail or over the telephone.

Data analysis

Quantitative data.

Participant votes on each PC-QI were calculated (median; interquartile range; percentile; percentile rank; mean and standard deviation). This data was used to determine whether consensus was met, variation in the data and the rank order. The rank order was determined by prioritising the voting outcomes using the median as the comparator score to determine the percentile for each PC-QI, then calculating the percentile rank by applying the following formula (percentile rank = percentile ÷ 100 × [n ∔ 1]). Variation was determined by assessing the means and standard deviations.

The research team agreed on a consensus standard prior to voting sessions taking place. PC-QIs that did not meet the consensus standard would be eliminated. The criteria for the a priori consensus standard were: median of the PC-QI must be ≥ 3 on the 5-point scale (1 = extremely unimportant, 5 = extremely important); and interquartile range of the PC-QI must be ≤ 2.

Qualitative data

Content analysis of qualitative data from the transcriptions was conducted by the lead investigator [ 24 ]. The intent was not to derive themes from the qualitative data, but to add further insights to participants’ rating of each PC-QI. The transcripts were reviewed to identify if there was text which explained or clarified the voting choice of participants. Keywords were not searched for in the text, rather the priority was the concept of clarifying the vote choice. Relevant texts were highlighted, extracted, and categorised by PC-QI and participant identification code; then reviewed in batches according to each PC-QI. The qualitative data was used to understand whether the strength of the discussion supporting specific PC-QIs was similar to that of the ranking of the PC-QIs in the quantitative outcomes. The data was not quantified for analysis.

Participants

Twenty-five people participated (face-to-face n  = 15; telephone n  = 10). Fifteen were recruited through information flyers mailed out by one aged care assessment service, five in response to newsletter advertisements, and five through advertisements at community-based carer support networks. There were 18 female participants (72%), and 24 were Caucasian (96%). All were aged between 66 to 90 years. Participants’ demographic characteristics are displayed in Table  4 .

Capacity to consent

All participants displayed capacity to consent using the Evaluation to Sign Consent Measure [ 22 ]. Whilst two participants’ carers were present during the session, using this measure to establish if the participant could provide their own consent without the support of their carer, enabled their participation and inclusion of their opinions.

Participants’ confirmation of results

All participants were asked to review their PC-QI record sheet and written transcript of the audiotaped session. Three out of 25 participants requested an amendment be made to their final vote (change of rating). In accordance with participants’ preferences, six participant responses were confirmed following telephone contact with the lead investigator, where results were presented and discussed, six were validated via hard copy sent in the mail, and 13 by email.

No PC-QIs were eliminated after the first round of participant votes (Table  5 ) in accordance with the consensus standard, however variation in the responses provided by participants was found and is displayed using the rank order of relative importance and standard deviation score for each PC-QI. Of the 24 PC-QIs, seven did not record any votes below four (1–5 scale). Furthermore, eight had a median of five (1–5 scale) indicating that overall, these PC-QIs were regarded as ‘extremely important’, and an interquartile range of zero, indicating there was no variability within the middle 50% of the data, with most participants voting similarly for these PC-QIs. Of the eight PC-QI’s, four represented the quality domain ‘respect for client’, three, ‘clear communication’ and one, ‘health care staff knowledge’.

Standard deviation scores across the 24 PC-QIs demonstrated variation in voting (ranging from 0.21 to 1.15). The six PC-QIs that showed the greatest variation, with a standard deviation score > 0.7 were: respecting a client’s cultural and/or religious preferences when booking an assessment; respecting a clients cultural and/or religious preferences during the assessment; providing the client adequate time to speak with the assessor; ensuring written information provided to a client about their care needs reflected dignity and respect; providing a client with written information that acknowledged spiritual and cultural preferences; and, explaining to the client an aged care delegate would decide what care type they were approved to access. Out of these six PC-QIs, three had a standard deviation of ≥ 1.0 which included: respecting a client’s cultural and/or religious preferences when booking an assessment; respecting a client’s cultural and/or religious preferences during an assessment and providing a client with written information that acknowledged spiritual and cultural preferences. Six PC-QIs had a standard deviation of ≤ 0.40 demonstrating the least variation and included: treating the client with dignity and respect at the time of booking the assessment; treating the client with dignity and respect during the assessment; treating the client as an equal partner in the care planning process during the assessment; providing the client with opportunities to make decisions about their care needs during the assessment; providing the client with an accurate support plan that reflected their needs and; advising the client who they can ask for assistance if their care needs change after the assessment has been completed.

After all the transcripts were reviewed, statements that aligned with each PC-QI were identified. Table 6 details these results and includes the five quality domains, PC-QIs that sit within each quality domain and the corresponding rank order determined by quantitative analysis. The qualitative findings confirmed the quantitative ranking order results.

To the best of our knowledge, this is the first study that defines a set of PC-QIs for Australian aged care assessment services using a consensus voting process that included current and future service users. The purpose of this was to ensure that the final set of PC-QIs better reflected what this group value as being of greatest importance when undergoing an assessment that determines their eligibility for, and access to, government-funded aged care services, confirming their person-centredness. Twenty-four evidence-based PC-QIs were quantitatively explored with participants, who voted on their perceived importance. All 24 PC-QIs met consensus on the first round of voting, indicating all were important to participants. While no PC-QIs were eliminated, variation within the ratings of some PC-QIs was observed. Qualitative data from audio recordings of participant voting sessions helps to explain the quantitative findings.

Involving people in research is becoming more important, with an increasing awareness of the value of involving them in the design, implementation and dissemination of health-related research [ 25 , 26 ]. Furthermore, involving people at the centre of care is a pre-requisite for person-centred health care and has been shown to result in equitable healthcare solutions and improved health outcomes [ 26 ]. One of the major objectives of person-centred care is the establishment of a working relationship between the person, their family and service delivery [ 27 ]. The inclusion of people in research with diversity such as a wide age range, is emerging internationally as an ethical imperative [ 28 ]. Including older people and communities in research about person centred aged care services has been shown to improve physical and social well-being outcomes for older people, with co-design being the gold standard [ 29 ]. The objective of this study was to reach consensus on PC-QIs for aged care assessment services from the end user’s perspective. The PC-QIs presented were confirmed by placing those people who may need to access aged care assessment services at some point in their lifetime, at the centre of the voting process to establish a relationship between the person, their family and the aged care assessment process. In addition, members of the eQC Patient and Carer Advisory Board who were involved in the development of the preliminary PC-QIs to assess their person-centredness, were engaged to provide their reflections on the research findings on the importance of PC-QIs for aged care assessment services, for this user group (Table  2 ).

This study highlighted two PC-QIs voted by participants to be of highest priority for aged care assessment services. These were: the provision of a support plan summary that reflected their needs, and, providing them with information about who to contact if their care needs change. The second highest priorities for participants were treating them with dignity and respect when booking their assessment and being able to understand the assessor during their assessment interview.

While it was voted as important to participants, respecting cultural and/or religious preferences at the time of booking an assessment, undertaking an assessment, and acknowledging spiritual and cultural preferences when providing written information demonstrated the greatest variation across responses. This suggests that the opinions of some participants in this study about cultural and spiritual preferences differs.

Similar findings were reported in a study that developed PC-QIs through co-design for primary care services in Alberta, Canada. Participants in that study prioritised five PC-QIs including involving patients in decisions about their treatment and care; a trusting relationship with health care provider; health information to support person-centred care; co-designing care in partnership with communities and overall experience [ 30 ].

QIs have been shown to assist clinicians, organisations, and policy makers by providing a quantitative basis to monitor the care and processes by which care is delivered to people [ 31 ]. PC-QIs ensure a person’s perspective is reflected, and that what is measured reflects what is most important to the person when receiving the care and services they need [ 15 ]. Through the inclusion of the perspectives of current and future service users, regarding what they believe to be important when delivering aged care assessment services, the 24 PC-QIs presented in this study provide quantitative measures that Australian aged care assessment services can use to monitor the quality of the services they provide to ensure it is person-centred. PC-QIs have been shown to assist with the standardisation of collecting and reporting of data at a systems level enabling actionability to influence change, and as such, can be used to monitor system performance, and evaluate policy and practice in relation to person-centred care [ 32 ]. The quality of aged care assessment services is governed by the Australian Government Department of Health and Aged Care in accordance with the Aged Care Assessment Quality Framework [ 10 ], and as such, is monitored at a systems level, rather than at the provider level.

A Quality of Care Experience in Aged Care Instrument was developed to routinely measure the quality of care experienced by older people in home and residential aged care settings to support the routine measurement of the quality of care experiences [ 33 , 34 ]. Patient experience measures are often used to measure performance at the provider level with little evidence of their use for system-level applications [ 30 ]. The 24 PC-QIs presented in this study support system-level applications and actionability to monitor aged care system performance related to eligibility and access to government-funded aged care services, while also including the service users’ voice.

The PC-QIs presented have applicability for aged care systems internationally. Many countries such as England, Scotland, United States, Canada, Singapore, Japan, Sweden, Finland, France, Denmark, the Netherlands and Germany, provide formal government funded-approved aged care for older people that is delivered by lead agencies in accordance with legislation that defines eligibility and access requirements and includes assessment of an older person’s needs [ 5 ]. Research findings presented in this study are relevant to understanding people’s views on what they perceive are quality assessment services that determine their care needs and eligibility to access appropriate aged care services, and bear relevance to those countries which undertake such assessments of older people.

Whilst a strength of this study is the involvement of older people who are current or future service users, one limitation of the study is that older people living in states and territories other than Queensland or in regional and remote areas of Queensland were not included. Another limitation is the low number of participants with a diagnosis of dementia and/or cognitive impairment, people from culturally and linguistically diverse backgrounds and those who identify as First Nations people. It is plausible that these groups may have different expectations regarding the quality of ACAT services and in recognition of this limitation, future work is required to explore the validity of these 24 PC-QIs in these population groups and for people living in other areas of Australia including regional and remote areas.

The 24 evidence-based PC-QIs presented in this study addresses the gap in PC-QI development for the assessment component of the Australian aged care system. The evidence-based PC-QIs confirmed by consensus voting by older people form the theoretical assessment of content validity. There is an opportunity for the Australian government to pilot test the 24 evidence-based PC-QIs to enable assessment of construct validity to support their operationalisation. This will assist the Australian government in beginning to move towards the standardisation of data collection and reporting across the assessment component of the Australian aged care system that enables the evaluation of policy and practice in relation to person-centred care. Of the 24 PC-QIs, seven did not receive any votes below four (1–5 scale). Furthermore, eight PC-QIs had a median of five and interquartile range of zero. Out of the 24 PC-QIs, five did not receive any votes below four, had a median of five, and an interquartile range of zero. It is therefore recommended that any data set should include these five PC-QIs at a minimum. These five PC-QIs represented the quality domains respect for client ( n  = 4), and clear communication ( n  = 1). There is an opportunity for the Australian government to move toward a comprehensive program that measures the quality of the Australian aged care system from eligibility through to delivery of care, through the inclusion of these PC-QIs in the current suite of quality measures. Additionally, the 24 PC-QIs presented support the objectives of the Australian Government regarding the provision of aged care services which include, appropriately meeting the needs of older Australians including being that of being person-centred.

Availability of data and materials

Data are available upon reasonable request from the authors.

Code availability

Not applicable.

Abbreviations

Aged Care Assessment Team

evaluating Quality of Care

Guidance for Reporting Involvement of Patients and the Public (revised)

Person Centred Quality Indicators

Quality Indicators

Residential Aged Care Facility

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Acknowledgements

We would like to thank all participants who took part in this research for openly sharing their views and opinions on the importance of person-centred quality indicators for aged care assessment services.

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S.S, C.T, MMK and N.R contributed equally to the conceptualisation of the research design and interpretation of the quantitative data. S.S facilitated all participant voting sessions, transcribed the qualitative data, analysed the qualitative and quantitative data and completed the initial draft of the manuscript. I.W, E.M and J.T (public contributors), contributed equally to reviewing and editing participant resources, reviewing and editing the draft manuscript and writing the consumer reflection in the discussion. All authors contributed to reviewing the final manuscript and approved the final version.

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Smith, S., Travers, C., Martin-Khan, M. et al. Person-centred quality indicators for Australian aged care assessment services: a mixed methods study. Res Involv Engagem 10 , 88 (2024). https://doi.org/10.1186/s40900-024-00606-x

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Received : 02 May 2024

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DOI : https://doi.org/10.1186/s40900-024-00606-x

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