chapter 3 method of research

How To Write The Methodology Chapter

The what, why & how explained simply (with examples).

By: Jenna Crossley (PhD) | Reviewed By: Dr. Eunice Rautenbach | September 2021 (Updated April 2023)

So, you’ve pinned down your research topic and undertaken a review of the literature – now it’s time to write up the methodology section of your dissertation, thesis or research paper . But what exactly is the methodology chapter all about – and how do you go about writing one? In this post, we’ll unpack the topic, step by step .

Overview: The Methodology Chapter

• The purpose  of the methodology chapter
• Why you need to craft this chapter (really) well
• How to write and structure the chapter
• Methodology chapter example
• Essential takeaways

What (exactly) is the methodology chapter?

The methodology chapter is where you outline the philosophical underpinnings of your research and outline the specific methodological choices you’ve made. The point of the methodology chapter is to tell the reader exactly how you designed your study and, just as importantly, why you did it this way.

Importantly, this chapter should comprehensively describe and justify all the methodological choices you made in your study. For example, the approach you took to your research (i.e., qualitative, quantitative or mixed), who  you collected data from (i.e., your sampling strategy), how you collected your data and, of course, how you analysed it. If that sounds a little intimidating, don’t worry – we’ll explain all these methodological choices in this post .

Why is the methodology chapter important?

The methodology chapter plays two important roles in your dissertation or thesis:

Firstly, it demonstrates your understanding of research theory, which is what earns you marks. A flawed research design or methodology would mean flawed results. So, this chapter is vital as it allows you to show the marker that you know what you’re doing and that your results are credible .

Secondly, the methodology chapter is what helps to make your study replicable. In other words, it allows other researchers to undertake your study using the same methodological approach, and compare their findings to yours. This is very important within academic research, as each study builds on previous studies.

The methodology chapter is also important in that it allows you to identify and discuss any methodological issues or problems you encountered (i.e., research limitations ), and to explain how you mitigated the impacts of these. Every research project has its limitations , so it’s important to acknowledge these openly and highlight your study’s value despite its limitations . Doing so demonstrates your understanding of research design, which will earn you marks. We’ll discuss limitations in a bit more detail later in this post, so stay tuned!

Need a helping hand?

How to write up the methodology chapter

First off, it’s worth noting that the exact structure and contents of the methodology chapter will vary depending on the field of research (e.g., humanities, chemistry or engineering) as well as the university . So, be sure to always check the guidelines provided by your institution for clarity and, if possible, review past dissertations from your university. Here we’re going to discuss a generic structure for a methodology chapter typically found in the sciences.

Before you start writing, it’s always a good idea to draw up a rough outline to guide your writing. Don’t just start writing without knowing what you’ll discuss where. If you do, you’ll likely end up with a disjointed, ill-flowing narrative . You’ll then waste a lot of time rewriting in an attempt to try to stitch all the pieces together. Do yourself a favour and start with the end in mind .

Section 1 – Introduction

As with all chapters in your dissertation or thesis, the methodology chapter should have a brief introduction. In this section, you should remind your readers what the focus of your study is, especially the research aims . As we’ve discussed many times on the blog, your methodology needs to align with your research aims, objectives and research questions. Therefore, it’s useful to frontload this component to remind the reader (and yourself!) what you’re trying to achieve.

In this section, you can also briefly mention how you’ll structure the chapter. This will help orient the reader and provide a bit of a roadmap so that they know what to expect. You don’t need a lot of detail here – just a brief outline will do.

Section 2 – The Methodology

The next section of your chapter is where you’ll present the actual methodology. In this section, you need to detail and justify the key methodological choices you’ve made in a logical, intuitive fashion. Importantly, this is the heart of your methodology chapter, so you need to get specific – don’t hold back on the details here. This is not one of those “less is more” situations.

Let’s take a look at the most common components you’ll likely need to cover. 

Methodological Choice #1 – Research Philosophy

Research philosophy refers to the underlying beliefs (i.e., the worldview) regarding how data about a phenomenon should be gathered , analysed and used . The research philosophy will serve as the core of your study and underpin all of the other research design choices, so it’s critically important that you understand which philosophy you’ll adopt and why you made that choice. If you’re not clear on this, take the time to get clarity before you make any further methodological choices.

While several research philosophies exist, two commonly adopted ones are positivism and interpretivism . These two sit roughly on opposite sides of the research philosophy spectrum.

Positivism states that the researcher can observe reality objectively and that there is only one reality, which exists independently of the observer. As a consequence, it is quite commonly the underlying research philosophy in quantitative studies and is oftentimes the assumed philosophy in the physical sciences.

Contrasted with this, interpretivism , which is often the underlying research philosophy in qualitative studies, assumes that the researcher performs a role in observing the world around them and that reality is unique to each observer . In other words, reality is observed subjectively .

These are just two philosophies (there are many more), but they demonstrate significantly different approaches to research and have a significant impact on all the methodological choices. Therefore, it’s vital that you clearly outline and justify your research philosophy at the beginning of your methodology chapter, as it sets the scene for everything that follows.

Methodological Choice #2 – Research Type

The next thing you would typically discuss in your methodology section is the research type. The starting point for this is to indicate whether the research you conducted is inductive or deductive .

Inductive research takes a bottom-up approach , where the researcher begins with specific observations or data and then draws general conclusions or theories from those observations. Therefore these studies tend to be exploratory in terms of approach.

Conversely , d eductive research takes a top-down approach , where the researcher starts with a theory or hypothesis and then tests it using specific observations or data. Therefore these studies tend to be confirmatory in approach.

Related to this, you’ll need to indicate whether your study adopts a qualitative, quantitative or mixed  approach. As we’ve mentioned, there’s a strong link between this choice and your research philosophy, so make sure that your choices are tightly aligned . When you write this section up, remember to clearly justify your choices, as they form the foundation of your study.

Methodological Choice #3 – Research Strategy

Next, you’ll need to discuss your research strategy (also referred to as a research design ). This methodological choice refers to the broader strategy in terms of how you’ll conduct your research, based on the aims of your study.

Several research strategies exist, including experimental , case studies , ethnography , grounded theory, action research , and phenomenology . Let’s take a look at two of these, experimental and ethnographic, to see how they contrast.

Experimental research makes use of the scientific method , where one group is the control group (in which no variables are manipulated ) and another is the experimental group (in which a specific variable is manipulated). This type of research is undertaken under strict conditions in a controlled, artificial environment (e.g., a laboratory). By having firm control over the environment, experimental research typically allows the researcher to establish causation between variables. Therefore, it can be a good choice if you have research aims that involve identifying causal relationships.

Ethnographic research , on the other hand, involves observing and capturing the experiences and perceptions of participants in their natural environment (for example, at home or in the office). In other words, in an uncontrolled environment.  Naturally, this means that this research strategy would be far less suitable if your research aims involve identifying causation, but it would be very valuable if you’re looking to explore and examine a group culture, for example.

As you can see, the right research strategy will depend largely on your research aims and research questions – in other words, what you’re trying to figure out. Therefore, as with every other methodological choice, it’s essential to justify why you chose the research strategy you did.

Methodological Choice #4 – Time Horizon

The next thing you’ll need to detail in your methodology chapter is the time horizon. There are two options here: cross-sectional and longitudinal . In other words, whether the data for your study were all collected at one point in time (cross-sectional) or at multiple points in time (longitudinal).

The choice you make here depends again on your research aims, objectives and research questions. If, for example, you aim to assess how a specific group of people’s perspectives regarding a topic change over time , you’d likely adopt a longitudinal time horizon.

Another important factor to consider is simply whether you have the time necessary to adopt a longitudinal approach (which could involve collecting data over multiple months or even years). Oftentimes, the time pressures of your degree program will force your hand into adopting a cross-sectional time horizon, so keep this in mind.

Methodological Choice #5 – Sampling Strategy

Next, you’ll need to discuss your sampling strategy . There are two main categories of sampling, probability and non-probability sampling.

Probability sampling involves a random (and therefore representative) selection of participants from a population, whereas non-probability sampling entails selecting participants in a non-random  (and therefore non-representative) manner. For example, selecting participants based on ease of access (this is called a convenience sample).

The right sampling approach depends largely on what you’re trying to achieve in your study. Specifically, whether you trying to develop findings that are generalisable to a population or not. Practicalities and resource constraints also play a large role here, as it can oftentimes be challenging to gain access to a truly random sample. In the video below, we explore some of the most common sampling strategies.

Methodological Choice #6 – Data Collection Method

Next up, you’ll need to explain how you’ll go about collecting the necessary data for your study. Your data collection method (or methods) will depend on the type of data that you plan to collect – in other words, qualitative or quantitative data.

Typically, quantitative research relies on surveys , data generated by lab equipment, analytics software or existing datasets. Qualitative research, on the other hand, often makes use of collection methods such as interviews , focus groups , participant observations, and ethnography.

So, as you can see, there is a tight link between this section and the design choices you outlined in earlier sections. Strong alignment between these sections, as well as your research aims and questions is therefore very important.

Methodological Choice #7 – Data Analysis Methods/Techniques

The final major methodological choice that you need to address is that of analysis techniques . In other words, how you’ll go about analysing your date once you’ve collected it. Here it’s important to be very specific about your analysis methods and/or techniques – don’t leave any room for interpretation. Also, as with all choices in this chapter, you need to justify each choice you make.

What exactly you discuss here will depend largely on the type of study you’re conducting (i.e., qualitative, quantitative, or mixed methods). For qualitative studies, common analysis methods include content analysis , thematic analysis and discourse analysis . In the video below, we explain each of these in plain language.

For quantitative studies, you’ll almost always make use of descriptive statistics , and in many cases, you’ll also use inferential statistical techniques (e.g., correlation and regression analysis). In the video below, we unpack some of the core concepts involved in descriptive and inferential statistics.

In this section of your methodology chapter, it’s also important to discuss how you prepared your data for analysis, and what software you used (if any). For example, quantitative data will often require some initial preparation such as removing duplicates or incomplete responses . Similarly, qualitative data will often require transcription and perhaps even translation. As always, remember to state both what you did and why you did it.

Section 3 – The Methodological Limitations

With the key methodological choices outlined and justified, the next step is to discuss the limitations of your design. No research methodology is perfect – there will always be trade-offs between the “ideal” methodology and what’s practical and viable, given your constraints. Therefore, this section of your methodology chapter is where you’ll discuss the trade-offs you had to make, and why these were justified given the context.

Methodological limitations can vary greatly from study to study, ranging from common issues such as time and budget constraints to issues of sample or selection bias . For example, you may find that you didn’t manage to draw in enough respondents to achieve the desired sample size (and therefore, statistically significant results), or your sample may be skewed heavily towards a certain demographic, thereby negatively impacting representativeness .

In this section, it’s important to be critical of the shortcomings of your study. There’s no use trying to hide them (your marker will be aware of them regardless). By being critical, you’ll demonstrate to your marker that you have a strong understanding of research theory, so don’t be shy here. At the same time, don’t beat your study to death . State the limitations, why these were justified, how you mitigated their impacts to the best degree possible, and how your study still provides value despite these limitations .

Section 4 – Concluding Summary

Finally, it’s time to wrap up the methodology chapter with a brief concluding summary. In this section, you’ll want to concisely summarise what you’ve presented in the chapter. Here, it can be a good idea to use a figure to summarise the key decisions, especially if your university recommends using a specific model (for example, Saunders’ Research Onion ).

Importantly, this section needs to be brief – a paragraph or two maximum (it’s a summary, after all). Also, make sure that when you write up your concluding summary, you include only what you’ve already discussed in your chapter; don’t add any new information.

Methodology Chapter Example

In the video below, we walk you through an example of a high-quality research methodology chapter from a dissertation. We also unpack our free methodology chapter template so that you can see how best to structure your chapter.

Wrapping Up

And there you have it – the methodology chapter in a nutshell. As we’ve mentioned, the exact contents and structure of this chapter can vary between universities , so be sure to check in with your institution before you start writing. If possible, try to find dissertations or theses from former students of your specific degree program – this will give you a strong indication of the expectations and norms when it comes to the methodology chapter (and all the other chapters!).

Also, remember the golden rule of the methodology chapter – justify every choice ! Make sure that you clearly explain the “why” for every “what”, and reference credible methodology textbooks or academic sources to back up your justifications.

If you need a helping hand with your research methodology (or any other component of your research), be sure to check out our private coaching service , where we hold your hand through every step of the research journey. Until next time, good luck!

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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• Knowledge Base
• Dissertation
• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

• The type of research you conducted
• How you collected and analyzed your data
• Any tools or materials you used in the research
• How you mitigated or avoided research biases
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

• Information bias
• Omitted variable bias
• Regression to the mean
• Survivorship bias
• Undercoverage bias
• Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyze?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

• The Hawthorne effect
• Observer bias
• The placebo effect
• Response bias and Nonresponse bias
• The Pygmalion effect
• Recall bias
• Social desirability bias
• Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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Chapter 3 The Research Process

In Chapter 1, we saw that scientific research is the process of acquiring scientific knowledge using the scientific method. But how is such research conducted? This chapter delves into the process of scientific research, and the assumptions and outcomes of the research process.

Paradigms of Social Research

Our design and conduct of research is shaped by our mental models or frames of references that we use to organize our reasoning and observations. These mental models or frames (belief systems) are called paradigms. The word “paradigm” was popularized by

Thomas Kuhn (1962) in his book The Structure of Scientific Revolutions, where he examined the history of the natural sciences to identify patterns of activities that shape the progress of science. Similar ideas are applicable to social sciences as well, where a social reality can be viewed by different people in different ways, which may constrain their thinking and reasoning about the observed phenomenon. For instance, conservatives and liberals tend to have very different perceptions of the role of government in people’s lives, and hence, have different opinions on how to solve social problems. Conservatives may believe that lowering taxes is the best way to stimulate a stagnant economy because it increases people’s disposable income and spending, which in turn expands business output and employment. In contrast, liberals may believe that governments should invest more directly in job creation programs such as public works and infrastructure projects, which will increase employment and people’s ability to consume and drive the economy. Likewise, Western societies place greater emphasis on individual rights, such as one’s right to privacy, right of free speech, and right to bear arms. In contrast, Asian societies tend to balance the rights of individuals against the rights of families, organizations, and the government, and therefore tend to be more communal and less individualistic in their policies. Such differences in perspective often lead Westerners to criticize Asian governments for being autocratic, while Asians criticize Western societies for being greedy, having high crime rates, and creating a “cult of the individual.” Our personal paradigms are like “colored glasses” that govern how we view the world and how we structure our thoughts about what we see in the world.

Paradigms are often hard to recognize, because they are implicit, assumed, and taken for granted. However, recognizing these paradigms is key to making sense of and reconciling differences in people’ perceptions of the same social phenomenon. For instance, why do liberals believe that the best way to improve secondary education is to hire more teachers, but conservatives believe that privatizing education (using such means as school vouchers) are more effective in achieving the same goal? Because conservatives place more faith in competitive markets (i.e., in free competition between schools competing for education dollars), while liberals believe more in labor (i.e., in having more teachers and schools). Likewise, in social science research, if one were to understand why a certain technology was successfully implemented in one organization but failed miserably in another, a researcher looking at the world through a “rational lens” will look for rational explanations of the problem such as inadequate technology or poor fit between technology and the task context where it is being utilized, while another research looking at the same problem through a “social lens” may seek out social deficiencies such as inadequate user training or lack of management support, while those seeing it through a “political lens” will look for instances of organizational politics that may subvert the technology implementation process. Hence, subconscious paradigms often constrain the concepts that researchers attempt to measure, their observations, and their subsequent interpretations of a phenomenon. However, given the complex nature of social phenomenon, it is possible that all of the above paradigms are partially correct, and that a fuller understanding of the problem may require an understanding and application of multiple paradigms.

Two popular paradigms today among social science researchers are positivism and post-positivism. Positivism , based on the works of French philosopher Auguste Comte (1798-1857), was the dominant scientific paradigm until the mid-20 th century. It holds that science or knowledge creation should be restricted to what can be observed and measured. Positivism tends to rely exclusively on theories that can be directly tested. Though positivism was originally an attempt to separate scientific inquiry from religion (where the precepts could not be objectively observed), positivism led to empiricism or a blind faith in observed data and a rejection of any attempt to extend or reason beyond observable facts. Since human thoughts and emotions could not be directly measured, there were not considered to be legitimate topics for scientific research. Frustrations with the strictly empirical nature of positivist philosophy led to the development of post-positivism (or postmodernism) during the mid-late 20 th century. Post-positivism argues that one can make reasonable inferences about a phenomenon by combining empirical observations with logical reasoning. Post-positivists view science as not certain but probabilistic (i.e., based on many contingencies), and often seek to explore these contingencies to understand social reality better. The post -positivist camp has further fragmented into subjectivists , who view the world as a subjective construction of our subjective minds rather than as an objective reality, and critical realists , who believe that there is an external reality that is independent of a person’s thinking but we can never know such reality with any degree of certainty.

Burrell and Morgan (1979), in their seminal book Sociological Paradigms and Organizational Analysis, suggested that the way social science researchers view and study social phenomena is shaped by two fundamental sets of philosophical assumptions: ontology and epistemology. Ontology refers to our assumptions about how we see the world, e.g., does the world consist mostly of social order or constant change. Epistemology refers to our assumptions about the best way to study the world, e.g., should we use an objective or subjective approach to study social reality. Using these two sets of assumptions, we can categorize social science research as belonging to one of four categories (see Figure 3.1).

If researchers view the world as consisting mostly of social order (ontology) and hence seek to study patterns of ordered events or behaviors, and believe that the best way to study such a world is using objective approach (epistemology) that is independent of the person conducting the observation or interpretation, such as by using standardized data collection tools like surveys, then they are adopting a paradigm of functionalism . However, if they believe that the best way to study social order is though the subjective interpretation of participants involved, such as by interviewing different participants and reconciling differences among their responses using their own subjective perspectives, then they are employing an interpretivism paradigm. If researchers believe that the world consists of radical change and seek to understand or enact change using an objectivist approach, then they are employing a radical structuralism paradigm. If they wish to understand social change using the subjective perspectives of the participants involved, then they are following a radical humanism paradigm.

Figure 3.1. Four paradigms of social science research (Source: Burrell and Morgan, 1979)

Figure 3.2. Functionalistic research process

The first phase of research is exploration . This phase includes exploring and selecting research questions for further investigation, examining the published literature in the area of inquiry to understand the current state of knowledge in that area, and identifying theories that may help answer the research questions of interest.

The first step in the exploration phase is identifying one or more research questions dealing with a specific behavior, event, or phenomena of interest. Research questions are specific questions about a behavior, event, or phenomena of interest that you wish to seek answers for in your research. Examples include what factors motivate consumers to purchase goods and services online without knowing the vendors of these goods or services, how can we make high school students more creative, and why do some people commit terrorist acts. Research questions can delve into issues of what, why, how, when, and so forth. More interesting research questions are those that appeal to a broader population (e.g., “how can firms innovate” is a more interesting research question than “how can Chinese firms innovate in the service-sector”), address real and complex problems (in contrast to hypothetical or “toy” problems), and where the answers are not obvious. Narrowly focused research questions (often with a binary yes/no answer) tend to be less useful and less interesting and less suited to capturing the subtle nuances of social phenomena. Uninteresting research questions generally lead to uninteresting and unpublishable research findings.

The next step is to conduct a literature review of the domain of interest. The purpose of a literature review is three-fold: (1) to survey the current state of knowledge in the area of inquiry, (2) to identify key authors, articles, theories, and findings in that area, and (3) to identify gaps in knowledge in that research area. Literature review is commonly done today using computerized keyword searches in online databases. Keywords can be combined using “and” and “or” operations to narrow down or expand the search results. Once a shortlist of relevant articles is generated from the keyword search, the researcher must then manually browse through each article, or at least its abstract section, to determine the suitability of that article for a detailed review. Literature reviews should be reasonably complete, and not restricted to a few journals, a few years, or a specific methodology. Reviewed articles may be summarized in the form of tables, and can be further structured using organizing frameworks such as a concept matrix. A well-conducted literature review should indicate whether the initial research questions have already been addressed in the literature (which would obviate the need to study them again), whether there are newer or more interesting research questions available, and whether the original research questions should be modified or changed in light of findings of the literature review. The review can also provide some intuitions or potential answers to the questions of interest and/or help identify theories that have previously been used to address similar questions.

Since functionalist (deductive) research involves theory-testing, the third step is to identify one or more theories can help address the desired research questions. While the literature review may uncover a wide range of concepts or constructs potentially related to the phenomenon of interest, a theory will help identify which of these constructs is logically relevant to the target phenomenon and how. Forgoing theories may result in measuring a wide range of less relevant, marginally relevant, or irrelevant constructs, while also minimizing the chances of obtaining results that are meaningful and not by pure chance. In functionalist research, theories can be used as the logical basis for postulating hypotheses for empirical testing. Obviously, not all theories are well-suited for studying all social phenomena. Theories must be carefully selected based on their fit with the target problem and the extent to which their assumptions are consistent with that of the target problem. We will examine theories and the process of theorizing in detail in the next chapter.

The next phase in the research process is research design . This process is concerned with creating a blueprint of the activities to take in order to satisfactorily answer the research questions identified in the exploration phase. This includes selecting a research method, operationalizing constructs of interest, and devising an appropriate sampling strategy.

Operationalization is the process of designing precise measures for abstract theoretical constructs. This is a major problem in social science research, given that many of the constructs, such as prejudice, alienation, and liberalism are hard to define, let alone measure accurately. Operationalization starts with specifying an “operational definition” (or “conceptualization”) of the constructs of interest. Next, the researcher can search the literature to see if there are existing prevalidated measures matching their operational definition that can be used directly or modified to measure their constructs of interest. If such measures are not available or if existing measures are poor or reflect a different conceptualization than that intended by the researcher, new instruments may have to be designed for measuring those constructs. This means specifying exactly how exactly the desired construct will be measured (e.g., how many items, what items, and so forth). This can easily be a long and laborious process, with multiple rounds of pretests and modifications before the newly designed instrument can be accepted as “scientifically valid.” We will discuss operationalization of constructs in a future chapter on measurement.

Simultaneously with operationalization, the researcher must also decide what research method they wish to employ for collecting data to address their research questions of interest. Such methods may include quantitative methods such as experiments or survey research or qualitative methods such as case research or action research, or possibly a combination of both. If an experiment is desired, then what is the experimental design? If survey, do you plan a mail survey, telephone survey, web survey, or a combination? For complex, uncertain, and multi-faceted social phenomena, multi-method approaches may be more suitable, which may help leverage the unique strengths of each research method and generate insights that may not be obtained using a single method.

Researchers must also carefully choose the target population from which they wish to collect data, and a sampling strategy to select a sample from that population. For instance, should they survey individuals or firms or workgroups within firms? What types of individuals or firms they wish to target? Sampling strategy is closely related to the unit of analysis in a research problem. While selecting a sample, reasonable care should be taken to avoid a biased sample (e.g., sample based on convenience) that may generate biased observations. Sampling is covered in depth in a later chapter.

At this stage, it is often a good idea to write a research proposal detailing all of the decisions made in the preceding stages of the research process and the rationale behind each decision. This multi-part proposal should address what research questions you wish to study and why, the prior state of knowledge in this area, theories you wish to employ along with hypotheses to be tested, how to measure constructs, what research method to be employed and why, and desired sampling strategy. Funding agencies typically require such a proposal in order to select the best proposals for funding. Even if funding is not sought for a research project, a proposal may serve as a useful vehicle for seeking feedback from other researchers and identifying potential problems with the research project (e.g., whether some important constructs were missing from the study) before starting data collection. This initial feedback is invaluable because it is often too late to correct critical problems after data is collected in a research study.

Having decided who to study (subjects), what to measure (concepts), and how to collect data (research method), the researcher is now ready to proceed to the research execution phase. This includes pilot testing the measurement instruments, data collection, and data analysis.

Pilot testing is an often overlooked but extremely important part of the research process. It helps detect potential problems in your research design and/or instrumentation (e.g., whether the questions asked is intelligible to the targeted sample), and to ensure that the measurement instruments used in the study are reliable and valid measures of the constructs of interest. The pilot sample is usually a small subset of the target population. After a successful pilot testing, the researcher may then proceed with data collection using the sampled population. The data collected may be quantitative or qualitative, depending on the research method employed.

Following data collection, the data is analyzed and interpreted for the purpose of drawing conclusions regarding the research questions of interest. Depending on the type of data collected (quantitative or qualitative), data analysis may be quantitative (e.g., employ statistical techniques such as regression or structural equation modeling) or qualitative (e.g., coding or content analysis).

The final phase of research involves preparing the final research report documenting the entire research process and its findings in the form of a research paper, dissertation, or monograph. This report should outline in detail all the choices made during the research process (e.g., theory used, constructs selected, measures used, research methods, sampling, etc.) and why, as well as the outcomes of each phase of the research process. The research process must be described in sufficient detail so as to allow other researchers to replicate your study, test the findings, or assess whether the inferences derived are scientifically acceptable. Of course, having a ready research proposal will greatly simplify and quicken the process of writing the finished report. Note that research is of no value unless the research process and outcomes are documented for future generations; such documentation is essential for the incremental progress of science.

Common Mistakes in Research

The research process is fraught with problems and pitfalls, and novice researchers often find, after investing substantial amounts of time and effort into a research project, that their research questions were not sufficiently answered, or that the findings were not interesting enough, or that the research was not of “acceptable” scientific quality. Such problems typically result in research papers being rejected by journals. Some of the more frequent mistakes are described below.

Insufficiently motivated research questions. Often times, we choose our “pet” problems that are interesting to us but not to the scientific community at large, i.e., it does not generate new knowledge or insight about the phenomenon being investigated. Because the research process involves a significant investment of time and effort on the researcher’s part, the researcher must be certain (and be able to convince others) that the research questions they seek to answer in fact deal with real problems (and not hypothetical problems) that affect a substantial portion of a population and has not been adequately addressed in prior research.

Pursuing research fads. Another common mistake is pursuing “popular” topics with limited shelf life. A typical example is studying technologies or practices that are popular today. Because research takes several years to complete and publish, it is possible that popular interest in these fads may die down by the time the research is completed and submitted for publication. A better strategy may be to study “timeless” topics that have always persisted through the years.

Unresearchable problems. Some research problems may not be answered adequately based on observed evidence alone, or using currently accepted methods and procedures. Such problems are best avoided. However, some unresearchable, ambiguously defined problems may be modified or fine tuned into well-defined and useful researchable problems.

Favored research methods. Many researchers have a tendency to recast a research problem so that it is amenable to their favorite research method (e.g., survey research). This is an unfortunate trend. Research methods should be chosen to best fit a research problem, and not the other way around.

Blind data mining. Some researchers have the tendency to collect data first (using instruments that are already available), and then figure out what to do with it. Note that data collection is only one step in a long and elaborate process of planning, designing, and executing research. In fact, a series of other activities are needed in a research process prior to data collection. If researchers jump into data collection without such elaborate planning, the data collected will likely be irrelevant, imperfect, or useless, and their data collection efforts may be entirely wasted. An abundance of data cannot make up for deficits in research planning and design, and particularly, for the lack of interesting research questions.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

• Chapter Summaries
• Project Prep
• Presentations
• Chapter Summary
• Research Topic
• Research Questions
• Research Methodology
• Research Methods

Summary of Chapter 3

Chapter 3 – the research process , paradigms of social research .

• People view social reality in different ways which can constrain their thinking and reasoning.
• Recognizing paradigms is important to see the difference in peoples’ perceptions of the same social phenomenon.
• Subconscious paradigms can interfere with research. 
• Positivism –  knowledge should be only based on what can be observed and measured. 
• Post-positivism – make reasonable inferences by using both observations and reasoning.
• Ontology – how we see the world.
• Functionalism – the world has a social order (ontology) and understanding patterns of events and behaviors is the best approach (epistemology).
• Interpretivism – study the social order through the subjective interpretation of participants.
• Radical Structuralism – seeks to understand or enact change using an objectivist approach.
• Radical humanism – wants to understand social change by using a subjective perspective.
• Scientists are primarily concerned with understanding generalizable behavior, events or phenomena, rather than idiosyncratic or changing events. 

“Our personal paradigms are like ‘colored glasses’ that govern how we view the world and how we structure our thoughts about what we see in the world” (Bhattacharjee, 2012,p. 17).

Overview of the Research Process 

• Observation – observe a phenomenon, event, behavior. 
• Rationalization –  make sense of what you observed.
• Validation – test the theory using the scientific method. 

Steps to the functionalist research process

• Research questions- specific questions about behavior, event, or phenomenon that you want answers to.
• What current knowledge is already available.
• Find key authors, articles, and finding in that area.
• Identify gaps in the knowledge already established.  
• Theories must be chosen based on their problem.
• Research design – plan to answer the research question established in the exploration phase. 
• Operationalization – establishing measurements for abstract constructs.
• Research method – how the researcher wishes to collect data.
• Sampling – taking a smaller portion of the population you are researching to make the experiment more feasible.
• Research proposal – document all the decisions made so far and the reasoning behind those decisions.
• Pilot testing –  helps find potential problems in the research and is ran on a small portion of the target audience.
• Data collection – using the sample population to collect data regarding the research question. 
• Data analysis – analyze the data and explain the conclusions you came to pertaining to the research question.
• Producing a research paper detailing the process and findings of your research.

Common Mistakes in Research 

• Research questions do not answer a problem that a large group of people experiences.
• Working on research that will lose value over time. 
• Using the wrong research method to collect data.
• Collecting data before establishing why the data was collected.
• Problems are not answered because there is not enough information

2 thoughts on “ Summary of Chapter 3 ”

[ I am assuming this is where I should put my Chapter Summary Response]

I was thrilled at the text’s invocation of Thomas Kuhn, then dismayed at the bastardization of Kuhn’s work – particularly the use of the term “paradigm” (a copy of Kuhn’s ‘The Structure of Scientific Revolutions’: https://www.lri.fr/~mbl/Stanford/CS477/papers/Kuhn-SSR-2ndEd.pdf )

More generalizable terms the text might have used: “conceptual schemes” or “conceptual framework”. But that would also dismiss Kuhn’s important (in my opinion) work on science:

“Normal science, the activity in which most scientists inevitably spend almost all their time, is predicated on the assumption that the scientific community knows what the world is like. Much of the success of the enterprise derives from the community’s willingness to defend that assumption, if necessary at considerable cost. Normal science, for example, often suppresses fundamental novelties because they are necessarily subversive of its basic commitments.” (Kuhn, 1962, p 5)

Kuhn’s “paradigm” isn’t a “set of colored glasses” – it is an entire world upon and through which scientific research and analysis is conducted.

Ironically, the text perfectly demonstrates this while it also seemingly contradicts itself: “A well-conducted literature review should indicate whether the initial research questions have already been addressed in the literature (which would obviate the need to study them again)” (p 21). Suggesting that once a “research question” has been addressed, there would be no value in revisiting the very same research is contradicted by the assertion (about Social Science) that “there is a high degree of measurement error in the social sciences and there is considerable uncertainty and little agreement on social science … higher levels of ambiguity, uncertainty, and error that come with such sciences, which merely reflects the high variability of social objects.” (p 2)

The text’s “paradigm” is that Social Science is both fixed (no need to revisit an existing established research question) and also “high[ly] variabl[e]”.

And this very incoherence is where Kuhn’s idea of a “paradigm shift” would be useful – that the degree to which a paradigm can’t sustain the contradictions, a new “paradigm” (scientific revolution) can emerge.

… Kenny

Hey girl!! You did a great job breaking down this chapter. For me, a lot of these concepts are a little more challenging. They really get you thinking about and questioning scientific research. I specifically struggled with paradigms, including positivism and post-positivism. While reading I had similar thoughts as Kenny, “isn’t this text also projecting the paradigm in which they see social science and natural science?”. I think you can enter a rabbit hole if you question the paradigms in which science is based.

Comments are closed.

This paper is in the following e-collection/theme issue:

Published on 11.4.2024 in Vol 26 (2024)

Evaluating the Digital Health Experience for Patients in Primary Care: Mixed Methods Study

Authors of this article:

Original Paper

• Melinda Ada Choy 1, 2 , BMed, MMed, DCH, MD   ; 
• Kathleen O'Brien 1 , BSc, GDipStats, MBBS, DCH   ; 
• Katelyn Barnes 1, 2 , BAPSC, MND, PhD   ; 
• Elizabeth Ann Sturgiss 3 , BMed, MPH, MForensMed, PhD   ; 
• Elizabeth Rieger 1 , BA, MClinPsych, PhD   ; 
• Kirsty Douglas 1, 2 , MBBS, DipRACOG, Grad Cert HE, MD  

1 School of Medicine and Psychology, College of Health and Medicine, The Australian National University, Canberra, Australia

2 Academic Unit of General Practice, Office of Professional Leadership and Education, ACT Health Directorate, Canberra, Australia

3 School of Primary and Allied Health Care, Monash University, Melbourne, Australia

Corresponding Author:

Melinda Ada Choy, BMed, MMed, DCH, MD

School of Medicine and Psychology

College of Health and Medicine

The Australian National University

Phone: 61 51244947

Email: [email protected]

Background: The digital health divide for socioeconomic disadvantage describes a pattern in which patients considered socioeconomically disadvantaged, who are already marginalized through reduced access to face-to-face health care, are additionally hindered through less access to patient-initiated digital health. A comprehensive understanding of how patients with socioeconomic disadvantage access and experience digital health is essential for improving the digital health divide. Primary care patients, especially those with chronic disease, have experience of the stages of initial help seeking and self-management of their health, which renders them a key demographic for research on patient-initiated digital health access.

Objective: This study aims to provide comprehensive primary mixed methods data on the patient experience of barriers to digital health access, with a focus on the digital health divide.

Methods: We applied an exploratory mixed methods design to ensure that our survey was primarily shaped by the experiences of our interviewees. First, we qualitatively explored the experience of digital health for 19 patients with socioeconomic disadvantage and chronic disease and second, we quantitatively measured some of these findings by designing and administering a survey to 487 Australian general practice patients from 24 general practices.

Results: In our qualitative first phase, the key barriers found to accessing digital health included (1) strong patient preference for human-based health services; (2) low trust in digital health services; (3) high financial costs of necessary tools, maintenance, and repairs; (4) poor publicly available internet access options; (5) reduced capacity to engage due to increased life pressures; and (6) low self-efficacy and confidence in using digital health. In our quantitative second phase, 31% (151/487) of the survey participants were found to have never used a form of digital health, while 10.7% (52/487) were low- to medium-frequency users and 48.5% (236/487) were high-frequency users. High-frequency users were more likely to be interested in digital health and had higher self-efficacy. Low-frequency users were more likely to report difficulty affording the financial costs needed for digital access.

Conclusions: While general digital interest, financial cost, and digital health literacy and empowerment are clear factors in digital health access in a broad primary care population, the digital health divide is also facilitated in part by a stepped series of complex and cumulative barriers. Genuinely improving digital health access for 1 cohort or even 1 person requires a series of multiple different interventions tailored to specific sequential barriers. Within primary care, patient-centered care that continues to recognize the complex individual needs of, and barriers facing, each patient should be part of addressing the digital health divide.

Introduction

The promise of ehealth.

The rapid growth of digital health, sped up by the COVID-19 pandemic and associated lockdowns, brings the promise of improved health care efficiency, empowerment of consumers, and health care equity [ 1 ]. Digital health is the use of information and communication technology to improve health [ 2 ]. eHealth, which is a type of digital health, refers to the use of internet-based technology for health care and can be used by systems, providers, and patients [ 2 ]. At the time of this study (before COVID-19), examples of eHealth used by patients in Australia included searching for web-based health information, booking appointments on the web, participating in online peer-support health forums, using mobile phone health apps (mobile health), emailing health care providers, and patient portals for electronic health records.

Digital health is expected to improve chronic disease management and has already shown great potential in improving chronic disease health outcomes [ 3 , 4 ]. Just under half of the Australian population (47.3%) has at least 1 chronic disease [ 5 ]. Rates of chronic disease and complications from chronic disease are overrepresented among those with socioeconomic disadvantage [ 6 ]. Therefore, patients with chronic disease and socioeconomic disadvantage have a greater need for the potential benefits of digital health, such as an improvement in their health outcomes. However, there is a risk that those who could benefit most from digital health services are the least likely to receive them, exemplifying the inverse care law in the digital age by Hart [ 7 ].

Our Current Understanding of the Digital Health Divide

While the rapid growth of digital health brings the promise of health care equity, it may also intensify existing inequities [ 8 ]. The digital health divide for socioeconomic disadvantage describes a pattern in which patients considered socioeconomically disadvantaged who are already marginalized through poor access to traditional health care are additionally hindered through poor access to digital health [ 9 ]. In Australia, only 67.4% of households in the lowest household income quintile have home internet access, compared to 86% of the general population and 96.9% of households in the highest household income quintile [ 10 ]. Survey-based studies have also shown that even with internet access, effective eHealth use is lower in populations considered disadvantaged, which speaks to broader barriers to digital health access [ 11 ].

The ongoing COVID-19 global pandemic has sped up digital health transitions with the rapid uptake of telephone and video consultations, e-prescription, and the ongoing rollout of e-mental health in Australia. These have supported the continuation of health care delivery while limiting physical contact and the pandemic spread; however, the early evidence shows that the digital health divide remains problematic. A rapid review identified challenges with reduced digital access and digital literacy among the older adults and racial and ethnic minority groups, which are both groups at greater health risk from COVID-19 infections [ 12 ]. An Australian population study showed that the rapid uptake of telehealth during peak pandemic was not uniform, with the older adults, very young, and those with limited English language proficiency having a lower uptake of general practitioner (GP) telehealth services [ 13 ].

To ensure that digital health improves health care outcome gaps, it is essential to better understand the nature and nuance of the digital health divide for socioeconomic disadvantage. The nature of the digital health divide for socioeconomic disadvantage has been explored primarily through quantitative survey data, some qualitative papers, a few mixed methods papers, and systematic reviews [ 11 , 14 - 16 ]. Identified barriers include a lack of physical hardware and adequate internet bandwidth, a reduced inclination to seek out digital health, and a low ability and confidence to use digital health effectively [ 16 ]. The few mixed methods studies that exist on the digital health divide generally triangulate quantitative and qualitative data on a specific disease type or population subgroup to draw a combined conclusion [ 17 , 18 ]. These studies have found digital health access to be associated with education, ethnicity, and gender as well as trust, complementary face-to-face services, and the desire for alternative sources of information [ 17 , 19 ].

What This Work Adds

This project sought to extend previous research by using an exploratory mixed methods design to ensure that the first step and driver of our survey of a larger population was primarily shaped by the experiences of our interviewees within primary care. This differs from the triangulation method, which places the qualitative and quantitative data as equal first contributors to the findings and does not allow one type of data to determine the direction of the other [ 18 ]. We qualitatively explored the experience of digital health for patients with socioeconomic disadvantage and chronic disease and then quantitatively measured some of the qualitative findings via a survey of the Australian general practice patient population. Our key objective was to provide comprehensive primary mixed methods data, describing the experience and extent of barriers to accessing digital health and its benefits, with a focus on the digital health divide. We completed this research in a primary care context to investigate a diverse community-based population with conceivable reasons to seek digital help in managing their health. Findings from this mixed methods study were intended to provide health care providers and policy makers with a more detailed understanding of how specific barriers affect different aspects or steps of accessing digital health. Ultimately, understanding digital health access can influence the future design and implementation of digital health services by more effectively avoiding certain barriers or building in enablers to achieve improved digital health access not only for everyone but also especially for those in need.

Study Design

We conducted a sequential exploratory mixed methods study to explore a complex phenomenon in depth and then measure its prevalence. We qualitatively explored the experience of digital health for patients with chronic disease and socioeconomic disadvantage in the first phase. Data from the first phase informed a quantitative survey of the phenomenon across a wider population in the second phase [ 18 ]. Both stages of research were conducted before the COVID-19 pandemic in Australia.

Recruitment

Qualitative phase participants.

The eligibility criteria for the qualitative phase were as follows: English-speaking adults aged ≥18 years with at least 1 self-reported chronic disease and 1 marker of socioeconomic disadvantage (indicated by ownership of a Health Care Card or receiving a disability pension, unemployment, or a user of public housing). A chronic disease was defined to potential participants as a diagnosed long-term health condition that had lasted at least 6 months (or is expected to last for at least 6 months; examples are listed in Multimedia Appendix 1 ). The markers of socioeconomic disadvantage we used to identify potential participants were based on criteria typically used by local general practices to determine which patients can have lower or no out-of-pocket expenses. Apart from unemployment, the 3 other criteria to identify socioeconomic disadvantage are means-tested government-allocated public social services [ 20 ]. Qualitative phase participants were recruited from May to July 2019 through 3 general practices and 1 service organization that serve populations considered socioeconomically disadvantaged across urban, regional, and rural regions in the Australian Capital Territory and South Eastern New South Wales. A total of 2 recruitment methods were used in consultation with and as per the choice of the participating organizations. Potential participants were either provided with an opportunity to engage with researchers (KB and MAC) in the general practice waiting room or identified by the practice or organization as suitable for an interview. Interested participants were given a detailed verbal and written description of the project in a private space before providing written consent to be interviewed. All interview participants received an Aus $50 (US $32.68) grocery shopping voucher in acknowledgment of their time.

Quantitative Phase Participants

Eligibility for the quantitative phase was English-speaking adults aged ≥18 years. The eligibility criteria for the quantitative phase were deliberately broader than those for the qualitative phase to achieve a larger sample size within the limitations of recruitment and with the intention that the factors of socioeconomic disadvantage and having a chronic disease could be compared to the digital health access of a more general population. The quantitative phase participants were recruited from November 2019 to February 2020. Study information and paper-based surveys were distributed and collected through 24 general practices across the Australian Capital Territory and South Eastern New South Wales regions, with an option for web-based completion.

Ethical Considerations

Qualitative and quantitative phase research protocols, including the participant information sheet, were approved by the Australian Capital Territory Health Human Research Ethics Committee (2019/ETH/00013) and the Australian National University Human Research Ethics Committee (2019/ETH00003). Qualitative phase participants were given a verbal and written explanation of the study, including how and when they could opt out, before they provided written consent. All interview participants received an Aus $50 (US $32.68) grocery shopping voucher in acknowledgment of their time. Quantitative participants were given a written explanation and their informed consent was implied by return of a completed survey. Participants in both phases of the study were told that all their data was deidentified. Consent was implied through the return of a completed survey.

Qualitative Data Collection and Analysis

Participants were purposively sampled to represent a range in age, gender, degree of socioeconomic disadvantage, and experience of digital health. The sampling and sample size were reviewed regularly by the research team as the interviews were being completed to identify potential thematic saturation.

The interview guide was developed by the research team based on a review of the literature and the patient dimensions of the framework of access by Levesque et al [ 21 ]. The framework by Levesque et al [ 21 ] is a conceptualization of health care access comprising 5 service and patient dimensions of accessibility and ability. The patient dimensions are as follows: (1) ability to perceive, (2) ability to seek, (3) ability to reach, (4) ability to pay, and (5) ability to engage [ 21 ]. The key interview topics included (1) digital health use and access, including facilitators and barriers; (2) attitudes toward digital health; and (3) self-perception of digital health skills and potential training. The interview guide was reviewed for face and content validity by the whole research team, a patient advocate, a digital inclusion charity representative, and the general practices where recruitment occurred. The questions and guide were iteratively refined by the research team to ensure relevance and support reaching data saturation. The interview guide has been provided as Multimedia Appendix 1 . The interviews, which took 45 minutes on average, were taped and transcribed. An interview summary sheet and reflective journal were completed by the interviewer after each interview to also capture nonverbal cues and tone.

Interview transcriptions were coded and processed by inductive thematic analysis. Data collection and analysis were completed in parallel to support the identification of data saturation. Data saturation was defined as no significant new information arising from new interviews and was identified by discussion with the research team [ 22 ]. The 2 interviewers (MAC and KB) independently coded the first 5 transcripts and reflected on them with another researcher (EAS) to ensure intercoder validity and reliability. The rest of the interviews were coded independently by the 2 interviewers, who regularly met to reflect on emerging themes and thematic saturation. Data saturation was initially indicated after 15 interviews and subsequently confirmed with a total of 19 interviews. Coding disagreements and theme development were discussed with at least 1 other researcher (EAS, ER, and KD). Thematic saturation and the final themes were agreed upon by the entire research team.

Quantitative Survey Development

The final themes derived in the qualitative phase of the project guided the specific quantitative phase research questions. The final themes were a list of ordered cumulative barriers experienced by participants in accessing digital health and its benefits ( Figure 1 ). The quantitative survey was designed to test the association between barriers to access and the frequency of use of digital health as a proxy measure for digital health access.

In the survey, the participants were asked about their demographic details, health and chronic diseases, knowledge, use and experience of digital health tools, internet access, perception of digital resource affordability, trust in digital health and traditional health services, perceived capability, health care empowerment, eHealth literacy, and relationship with their GP.

Existing scales and questions from the literature and standardized Australian-based surveys were used whenever possible. We used selected questions and scales from the Australian Bureau of Statistics standards, the eHealth Literacy Scale (eHEALS), the eHealth Literacy Questionnaire, and the Southgate Institute for Health Society and Equity [ 17 , 23 - 26 ]. We adapted other scales from the ICEpop Capability Measure for Adults, the Health Care Empowerment Inventory (HCEI), the Patient-Doctor Relationship Questionnaire, and the Chao continuity questionnaire [ 23 , 27 - 29 ]. Where an existing scale to measure a barrier or theme did not exist, the research team designed the questions based on the literature. Our questions around the frequency of digital health use were informed by multiple existing Australian-based surveys on general technology use [ 30 , 31 ]. Most of the questions used a Likert scale. Every choice regarding the design, adaptation, or copy of questions for the survey was influenced by the qualitative findings and decided on by full agreement among the 2 researchers who completed and coded the interviews. A complete copy of the survey is provided in Multimedia Appendix 2 .

Pilot-testing of the survey was completed with 5 patients, 2 experts on digital inclusion, and 3 local GPs for both the paper surveys and web-based surveys via Qualtrics Core XM (Qualtrics LLC). The resulting feedback on face and content validity, functionality of the survey logic, and feasibility of questionnaire completion was incorporated into the final version of the survey.

The survey was offered on paper with a participant information sheet, which gave the patients the option to complete the web-based survey. The survey was handed out to every patient on paper to avoid sampling bias through the exclusion of participants who could not complete the web-based survey [ 32 ].

Quantitative Data Treatment and Analysis

Data were exported from Qualtrics Core XM to an SPSS (version 26; IBM Corp) data set. Data cleaning and screening were undertaken (KB and KO).

Descriptive statistics (number and percentage) were used to summarize participant characteristics, preference measures, and frequency of eHealth use. Significance testing was conducted using chi-square tests, with a threshold of P <.05; effect sizes were measured by the φ coefficient for 2×2 comparisons and Cramer V statistic for all others. Where the cells sizes were too small, the categories were collapsed for the purposes of significance testing. The interpretation of effect sizes was as per the study by Cohen [ 33 ]. The analysis was conducted in SPSS and SAS (version 9.4; SAS Institute).

Participant Characteristics

Participants’ self-reported characteristics included gender, indigenous status, income category, highest level of education, marital status, and language spoken at home.

Age was derived from participant-reported year of birth and year of survey completion as of 2019 and stratified into age groups. The state or territory of residence was derived from the participant-reported postcode. The remoteness area was derived using the postcode reported by the participants and mapped to a modified concordance from the Australian Bureau of Statistics. Occupation-free text responses were coded using the Australian Bureau of Statistics Census statistics level 1 and 2 descriptors. The country of birth was mapped to Australia, other Organisation for Economic Cooperation and Development countries, and non–Organisation for Economic Cooperation and Development countries.

Frequency of eHealth Use

A summary measure of the frequency of eHealth use was derived from the questions on the use of different types of eHealth.

Specifically, respondents were asked if they had ever used any form of web-based health (“eHealth“) and, if so, to rate how often (never, at least once, every now and then, and most days) against 6 types of “eHealth” (searching for health information online, booking appointments online, emailing health care providers, using health-related mobile phone apps, accessing My Health Record, and accessing online health forums). The frequency of eHealth use was then classified as follows:

• High user: answered “most days” to at least 1 question on eHealth use OR answered “every now and then” to at least 2 questions on eHealth use
• Never user: answered “no” to having ever used any form of eHealth OR “never” to all 6 questions on eHealth use
• Low or medium user: all other respondents.

The frequency of eHealth use was reported as unweighted descriptive statistics (counts and percentages) against demographic characteristics and for the elements of each of the themes identified in phase 1.

Overview of Key Themes

Data were reported against the 6 themes from the phase 1 results of preference, trust, cost, structural access, capacity to engage, and self-efficacy. Where the components of trust, cost, capacity to engage, and self-efficacy had missing data (for less than half of the components only), mean imputation was used to minimize data loss. For each theme, the analysis excluded those for whom the frequency of eHealth use was unknown.

Preference measures (survey section D1 parts 1 to 3) asked participants to report against measures with a 4-point Likert scale (strongly disagree, disagree, agree, and strongly agree). Chi-square tests were conducted after the categories were condensed into 2 by combining strongly disagree and as well as combining strongly agree and agree.

Summary measures for trust were created in 4 domains: trust from the eHealth Literacy Questionnaire (survey section D1 parts 4 to 8), trust from Southgate—GPs, specialists, or allied health (survey section D2 parts 1 to 5), trust from Southgate—digital health (survey section D2 parts 6, 7, 9, and 10), and trust from Southgate—books or pamphlets (survey section D2 part 8). The data were grouped as low, moderate, and high trust based on the assigned scores from the component data. Chi-square tests were conducted comparing low-to-moderate trust against high trust for GP, specialists, or allied health and comparing low trust against moderate-to-high trust for book or pamphlet.

Summary measures for cost were created from survey item C10. To measure cost, participants were asked about whether they considered certain items or services to be affordable. These included cost items mentioned in the qualitative phase interviews relating to mobile phones (1 that connects to the internet, 1 with enough memory space to download apps, downloads or apps requiring payment, repairs, and maintenance costs), having an iPad or tablet with internet connectivity, a home computer or laptop (owning, repairs, and maintenance), home fixed internet access, and an adequate monthly data allowance. These 9 items were scored as “yes definitely”=1 or 0 otherwise. Chi-square tests were conducted with never and low or medium eHealth users combined.

Structural Access

Structural access included asking where the internet is used by participants (survey section C8) and factors relating to internet access (survey section C8 parts 1-3) reporting against a 4-point Likert scale (strongly disagree, disagree, agree, and strongly agree). Chi-square tests were conducted with strongly disagree, disagree, agree, or strongly agree, and never, low, or medium eHealth use combined.

Capacity to Engage

Summary measures for capacity to engage were created from survey section E1. To measure the capacity to engage, participants were asked about feeling “settled and secure,” “being independent,” and “achievement and progress” as an adaptation of the ICEpop Capability Measure for Adults [ 27 ], reporting against a 4-point Likert-like scale. Responses were scored from 1 (“I am unable to feel settled and secure in any areas of my life”) to 4 (“I am able to feel settled and secure in all areas of my life”).

The summary capacity measure was derived by the summation of responses across the 3 questions, which were classified into 4 groups, A to D, based on these scores. Where fewer than half of the responses were missing, mean imputation was used; otherwise, the record was excluded. Groups A and B were combined for significance testing.

Self-Efficacy

Summary measures for self-efficacy were adapted from the eHEALS (E3) and the HCEI (E2) [ 23 , 24 ].

Survey section E3—eHEALS—comprised 8 questions, with participants reporting against a 5-point Likert scale for each (strongly disagree, disagree, neither, agree, and strongly agree). These responses were assigned 1 to 5 points, respectively. The summary eHEALS measure was derived by the summation of responses across the 8 questions, which were classified into 5 groups, A to E, based on these scores. Where fewer than half of the responses were missing, mean imputation was used; otherwise, the record was excluded. Groups A to C and D to E were combined for significance testing.

Survey section E2—HCEI—comprised 5 questions, with participants reporting against a 5-point Likert scale for each (strongly disagree, disagree, neither, agree, and strongly agree). Strongly disagree and disagree and neither were combined, and similarly agree and strongly agree were combined for significance testing.

Qualitative Results

The demographic characteristics of the patients that we interviewed are presented in Table 1 .

The key barriers found to accessing digital health included (1) strong patient preference for human-based health services; (2) low trust in digital health services; (3) high financial costs of necessary tools, maintenance, and repairs; (4) poor publicly available internet access options; (5) reduced capacity to engage due to increased life pressures; and (6) low self-efficacy and confidence in using digital health.

Rather than being an equal list of factors, our interviewees described these barriers as a stepped series of cumulative hurdles, which is illustrated in Figure 1 . Initial issues of preference and trust were foundational to a person even when considering the option of digital health, while digital health confidence and literacy were barriers to full engagement with and optimal use of digital health. Alternatively, interviewees who did use digital health had been enabled by the same factors that were barriers to others.

a GP: general practitioner.

b Multiple answers per respondent.

Strong Patient Preference for Human-Based Health Services

Some patients expressed a strong preference for human-based health services rather than digital health services. In answer to a question about how digital health services could be improved, a patient said the following:

Well, having an option where you can actually bypass actually having to go through the app and actually talk directly to someone. [Participant #10]

For some patients, this preference for human-based health services appeared to be related to a lack of exposure to eHealth. These patients were not at all interested in or had never thought about digital health options. A participant responded the following to the interviewer’s questions:

Interviewer: So when...something feels not right, how do you find out what’s going on?

Respondent: I talk to Doctor XX.

Interviewer: Do you ever Google your symptoms or look online for information?

Respondent: No, I have never even thought of doing that actually. [Participant #11]

For other patients, their preference for human-based health care stemmed from negative experiences with technology. These patients reported actively disliking computers and technology in general and were generally frustrated with what they saw as the pitfalls of technology. A patient stated the following:

If computers and internet weren’t so frigging slow because everything is on like the slowest speed network ever and there’s ads blocking everything. Ads, (expletive) ads. [Participant #9]

A patient felt that he was pushed out of the workforce due his inability to keep up with technology-based changes and thus made a decision to never own a computer:

But, you know, in those days when I was a lot younger those sorts of things weren’t about and they’re just going ahead in leaps and bounds and that’s one of the reasons why I retired early. I retired at 63 because it was just moving too fast and it’s all computers and all those sorts of things and I just couldn’t keep up. [Participant #17]

Low Trust in Digital Health Services

Several patients described low trust levels for digital and internet-based technology in general. Their low trust was generally based on stories they had heard of other people’s negative experiences. A patient said the following:

I don’t trust the internet to be quite honest. You hear all these stories about people getting ripped off and I’ve worked too hard to get what I’ve got rather than let some clown get it on the internet for me. [Participant #11]

Some of this distrust was specific to eHealth. For example, some patients were highly suspicious of the government’s motives with regard to digital health and were concerned about the privacy of their health information, which made them hesitant about the concept of a universal electronic health record. In response to the interviewer’s question, a participant said the following:

Interviewer: Are there any other ways you think that eHealth might help you?

Respondent: I’m sorry but it just keeps coming back to me, Big Brother. [Participant #7]

Another participant said the following:

I just would run a mile from it because I just wouldn’t trust it. It wouldn’t be used to, as I said, for insurance or job information. [Participant #16]

High Financial Costs of the Necessary Tools, Maintenance, and Repairs

A wide variety of patients described affordability issues across several different aspects of the costs involved in digital health. They expressed difficulty in paying for the following items: a mobile phone that could connect to the internet, a mobile phone with enough memory space to download apps, mobile phone apps requiring extra payment without advertisements, mobile phone repair costs such as a broken screen, a computer or laptop, home internet access, and adequate monthly data allowance and speeds to functionally use the internet. Current popular payment systems, such as plans, were not feasible for some patients. A participant stated the following:

I don’t have a computer...I’m not in the income bracket to own a computer really. Like I could, if I got one on a plan kind of thing or if I saved up for x-amount of time. But then like if I was going on the plan I’d be paying interest for having it on like lay-buy kind of thing, paying it off, and if it ever got lost or stolen I would still have to repay that off, which is always a hassle. And yeah. Yeah, I’m like financially not in the state where I’m able to...own a computer right now as I’m kind of paying off a number of debts. [Participant #9]

Poor Publicly Available Internet Access Options

Some patients described struggling without home internet access. While they noted some cost-free public internet access points, such as libraries, hotel bars, and restaurants, they often found these to be inconvenient, lacking in privacy, and constituting low-quality options for digital health. A patient stated the following:

...it’s incredibly slow at the library. And I know why...a friend I went to school with used to belong to the council and the way they set it up, they just got the raw end of the stick and it is really, really slow. It’s bizarre but you can go to the X Hotel and it’s heaps quicker. [Participant #15]

In response to the interviewer's question, a participant said the following:

Interviewer: And do you feel comfortable doing private stuff on computers at the library...?

Respondent: Not really, no, but I don’t have any other choice, so, yeah. [Participant #9]

Reduced Capacity to Engage Due to Increased Life Pressures

When discussing why they were not using digital health or why they had stopped using digital health, patients often described significant competing priorities and life pressures that affected their capacity to engage. An unemployed patient mentioned that his time and energy on the internet were focused primarily on finding work and that he barely had time to focus on his health in general, let alone engage in digital health.

Other patients reported that they often felt that their ability to learn about and spend time on digital health was taken up by caring for sick family members, paying basic bills, or learning English. Some patients said that the time they would have spent learning digital skills when they were growing up had been lost to adverse life circumstances such as being in jail:

So we didn’t have computers in the house when I was growing up. And I didn’t know I’ve never...I’ve been in and out of jail for 28 odd years so it sort of takes away from learning from this cause it’s a whole different… it’s a whole different way of using a telephone from a prison. [Participant #11]

Low Self-Efficacy and Confidence in Starting the Digital Health Process

Some patients had a pervasive self-perception of being slow learners and being unable to use technology. Their stories of being unconfident learners seemed to stem from the fact that they had been told throughout their lives that they were intellectually behind. A patient said the following:

The computer people...wouldn’t take my calls because I’ve always been dumb with that sort of stuff. Like I only found out this later on in life, but I’m actually severely numerically dyslexic. Like I have to triple-check everything with numbers. [Participant #7]

Another patient stated the following:

I like went to two English classes like a normal English class with all the kids and then another English class with about seven kids in there because I just couldn’t I don’t know maybe because I spoke another language at home and they sort of like know I was a bit backward. [Participant #6]

These patients and others had multiple missing pieces of information that they felt made it harder to engage in digital health compared to “easier” human-based services. A patient said the following:

Yeah I’ve heard of booking online but I just I don’t know I find it easier just to ring up. And I’ll answer an email from a health care provider but I wouldn’t know where to start to look for their email address. [Participant #11]

In contrast, the patients who did connect with digital health described themselves as independent question askers and proactive people. Even when they did not know how to use a specific digital health tool, they were confident in attempting to and asking for help when they needed it. A patient said the following:

I’m a “I will find my way through this, no matter how long it takes me” kind of person. So maybe it’s more my personality...If I have to ask for help from somewhere, wherever it is, I will definitely do that. [Participant #3]

Quantitative Results

A total of 487 valid survey responses were received from participants across 24 general practices. The participant characteristics are presented in detail in Table S1 in Multimedia Appendix 3 .

The mean age of the participants was approximately 50 years (females 48.9, SD 19.4 years; males 52.8, SD 20.0 years), and 68.2% (332/487) of the participants identified as female. Overall, 34.3% (151/439) of respondents reported never using eHealth, and 53.8% (236/439) reported high eHealth use.

There were statistically significant ( P <.05) differences in the frequency of eHealth use in terms of age group, gender, state, remoteness, highest level of education, employment status, occupation group, marital status, and language spoken at home, with effect sizes being small to medium. Specifically, high eHealth characteristics were associated with younger age, being female, living in an urban area, and being employed.

Table 2 presents the frequency of eHealth use against 3 internet preference questions.

Preference for using the internet and technology in general and for health needs in particular were significantly related to the frequency of eHealth use ( P <.05 for each), with the effect sizes being small to medium.

a Excludes those for whom frequency of eHealth use is unknown.

b Chi-square tests conducted with strongly disagree and disagree combined, and agree and strongly agree combined.

Table 3 presents the frequency of eHealth use against 4 measures of trust.

The degree of trust was not statistically significantly different for the frequency of eHealth use for any of the domains.

b eHLQ: eHealth Literacy Questionnaire.

c Derived from survey question D1, parts 4 to 8. Mean imputation used where ≤2 responses were missing. If >2 responses were missing, the records were excluded.

d Derived from survey question D2, parts 1 to 5. Mean imputation used where ≤2 responses were missing. If >2 responses were missing, the records were excluded.

e Chi-square test conducted comparing low-to-moderate trust against high trust.

f Derived from survey question D2, parts 6, 7, 9, and 10. Mean imputation used where ≤2 responses were missing. If >2 responses were missing, the records were excluded.

g Derived from survey question D2 part 8.

h Chi-square test conducted comparing low trust against moderate-to-high trust.

Affordability of items and services was reported as No cost difficulty or Cost difficulty. eHealth frequency of use responses were available for 273 participants; among those with no cost difficulty , 1% (2/204) were never users, 14.2% (29/204) were low or medium users, and 84.8% (173/204) were high users of eHealth; among those with cost difficulty , 1% (1/69) were never users, 26% (18/69) were low or medium users, and 73% (50/69) were high users. There was a statistically significant difference in the presence of cost as a barrier between never and low or medium eHealth users compared to high users ( χ 2 1 =5.25; P =.02), although the effect size was small.

Table 4 presents the frequency of eHealth use for elements of structural access.

Quality of internet access and feeling limited in access to the internet were significantly associated with frequency of eHealth use ( P <.05), although the effect sizes were small.

b N/A: not applicable (cell sizes insufficient for chi-square test).

c Chi-square tests conducted with strongly disagree and disagree combined, agree and strongly agree combined, and never and low or medium categories combined.

Table 5 presents the frequency of eHealth use against respondents’ capacity to engage.

Capacity to engage was not significantly different for the frequency of eHealth use ( P =.54). 

b Derived from survey item E1. Where 1 response was missing, the mean imputation was used. If >1 response was missing, the record was excluded.

c Chi-square tests conducted with groups A and B combined.

Table 6 presents the frequency of eHealth use for elements of self-efficacy.

Statistically significant results were observed for the relationship between self-efficacy by eHEALS (moderate effect size) and frequency of eHealth use as well as for some of the questions from the HCEI (reliance on health professionals or others to access and explain information; small effect size; P <.05).

b eHEALS: eHealth Literacy Scale.

c eHEALS derived from item E3 (8 parts). Where ≤ 4 responses were missing, mean imputation was used. If >4 responses were missing, the records were excluded. Groups A to C as well as groups D to E were combined for the chi-square test.

d Strongly disagree, disagree, neither, and agree or strongly agree combined for significance testing.

Principal Findings

This paper reports on the findings of a sequential exploratory mixed methods study on the barriers to digital health access for a group of patients in Australian family medicine, with a particular focus on chronic disease and socioeconomic disadvantage.

In the qualitative first phase, the patients with socioeconomic disadvantage and chronic disease described 6 cumulative barriers, as demonstrated in Figure 1 . Many nonusers of digital health preferred human-based services and were not interested in technology, while others were highly suspicious of the technology in general. Some digitally interested patients could not afford quality hardware and internet connectivity, a barrier that was doubled by low quality and privacy when accessing publicly available internet connections. Furthermore, although some digitally interested patients had internet access, their urgent life circumstances left scarce opportunity to access digital health and develop digital health skills and confidence.

In our quantitative second phase, 31% (151/487) of the survey participants from Australian general practices were found to have never used a form of digital health. Survey participants were more likely to use digital health tools frequently when they also had a general digital interest and a digital health interest. Those who did not frequently access digital health were more likely to report difficulty affording the financial costs needed for digital access. The survey participants who frequently accessed digital health were more likely to have high eHealth literacy and high levels of patient empowerment.

Comparison With Prior Work

In terms of general digital health access, the finding that 31% (151/487) of the survey participants had never used one of the described forms of eHealth is in keeping with an Australian-based general digital participation study that found that approximately 9% of the participants were nonusers and 17% rarely engaged with the internet at all [ 34 ]. With regard to the digital health divide, another Australian-based digital health divide study found that increased age, living in a lower socioeconomic area, being Aboriginal or Torres Strait Islander, being male, and having no tertiary education were factors negatively associated with access to digital health services [ 17 ]. Their findings correspond to our findings that higher-frequency users of eHealth were associated with younger age, being female, living in an urban area, and being employed. Both studies reinforce the evidence of the digital health divide based on gender, age, and socioeconomic disadvantage in Australia.

With regard to digital health barriers, our findings provide expanded details on the range of digital health items and services that present a cost barrier to consumers. Affordability is a known factor in digital access and digital health access, and it is measured often by general self-report or relative expenditure on internet access to income [ 30 ]. Our study revealed the comprehensive list of relevant costs for patients. Our study also demonstrated factors of cost affordability beyond the dollar value of an item, as interviewees described the struggle of using slow public internet access without privacy features and the risks involved in buying a computer in installments. When we reflected on the complexity and detail of the cost barrier in our survey, participants demonstrated a clear association between cost and the frequency of digital health use. This suggests that a way to improve digital health access for some people is to improve the quality, security, and accessibility of public internet access options as well as to provide free or subsidized hardware, internet connection, and maintenance options for those in need, work that is being done by at least 1 digital inclusion charity in the United Kingdom [ 35 ].

Many studies recognize the factors of eHealth literacy and digital confidence for beneficial digital health access [ 36 ]. Our interviews demonstrated that some patients with socioeconomic disadvantage have low digital confidence, but that this is often underlined by a socially reinforced lifelong low self-confidence in their intellectual ability. In contrast, active users, regardless of other demographic factors, described themselves as innately proactive question askers. This was reinforced by our finding of a relationship between health care empowerment and the frequency of eHealth use. This suggests that while digital health education and eHealth literacy programs can improve access for some patients, broader and deeper long-term solutions addressing socioeconomic drivers of digital exclusion are needed to improve digital health access for some patients with socioeconomic disadvantage [ 8 ]. The deep permeation of socially enforced low self-confidence and lifelong poverty experienced by some interviewees demonstrate that the provision of free hardware and a class on digital health skills can be, for some, a superficial offering when the key underlying factor is persistent general socioeconomic inequality.

The digital health divide literature tends to identify the digital health divide, the factors and barriers that contribute to it, and the potential for it to widen if not specifically addressed [ 16 ]. Our findings have also identified the divide and the barriers, but what this study adds through our qualitative phase in particular is a description of the complex interaction of those barriers and the stepped nature of some of those barriers as part of the individual’s experience in trying to access digital health.

Strengths and Limitations

A key strength of this study is the use of a sequential exploratory mixed methods design. The initial qualitative phase guided a phenomenological exploration of digital health access experiences for patients with chronic disease and socioeconomic disadvantage. Our results in both study phases stem from the patients’ real-life experiences of digital health access. While some of our results echo the findings of other survey-based studies on general digital and digital health participation, our method revealed a greater depth and detail of some of these barriers, as demonstrated in how our findings compare to prior work.

As mentioned previously, the emphasis of this study on the qualitative first phase is a strength that helped describe the interactions between different barriers. The interviewees described their experiences as cumulative unequal stepped barriers rather than as producing a nonordered list of equal barriers. These findings expand on the known complexity of the issue of digital exclusion and add weight to the understanding that improving digital health access needs diverse, complex solutions [ 17 ]. There is no panacea for every individual’s digital health access, and thus, patient-centered digital health services, often guided by health professionals within the continuity of primary care, are also required to address the digital health divide [ 37 ].

While the sequential exploratory design is a strength of the study, it also created some limitations for the second quantitative phase. Our commitment to using the qualitative interview findings to inform the survey questions meant that we were unable to use previously validated scales for every question and that our results were less likely to lead to a normal distribution. This likely affected our ability to demonstrate significant associations for some barriers. We expect that further modeling is required to control for baseline characteristics and determine barrier patterns for different types of users.

One strength of this study is that the survey was administered to a broad population of Australian family medicine patients with diverse patterns of health via both paper-based and digital options. Many other digital health studies use solely digital surveys, which can affect the sample. However, we cannot draw conclusions from our survey about patients with chronic disease due to the limitations of the sample size for these subgroups.

Another sample-based limitation of this study was that our qualitative population did not include anyone aged from 18 to 24 years, despite multiple efforts to recruit. Future research will hopefully address this demographic more specifically.

While not strictly a limitation, we recognize that because this research was before COVID-19, it did not include questions about telehealth, which has become much more mainstream in recent years. The patients may also have changed their frequency of eHealth use because of COVID-19 and an increased reliance on digital services in general. Future work in this area or future versions of this survey should include telehealth and acknowledge the impact of COVID-19. However, the larger concept of the digital health divide exists before and after COVID-19, and in fact, our widespread increased reliance on digital services makes the digital divide an even more pressing issue [ 12 ].

Conclusions

The experience of digital health access across Australian primary care is highly variable and more difficult to access for those with socioeconomic disadvantage. While general digital interest, financial cost, and digital health literacy and empowerment are clear factors in digital health access in a broad primary care population, the digital health divide is also facilitated in part by a stepped series of complex and cumulative barriers.

Genuinely improving digital health access for 1 cohort or even 1 person requires a series of multiple different interventions tailored to specific sequential barriers. Given the rapid expansion of digital health during the global COVID-19 pandemic, attention to these issues is necessary if we are to avoid entrenching inequities in access to health care. Within primary care, patient-centered care that continues to recognize the complex individual needs of, and barriers facing, each patient should be a part of addressing the digital health divide.

Acknowledgments

The authors are thankful to the patients who shared their experiences with them via interview and survey completion. The authors are also very grateful to the general practices in the Australian Capital Territory and New South Wales who kindly gave their time and effort to help organize interviews, administer, and post surveys in the midst of the stress of day-to-day practice life and the bushfires of 2018-2019. The authors thank and acknowledge the creators of the eHealth Literacy Scale, the eHealth Literacy Questionnaire, the ICEpop Capability Measure for Adults, the Health Care Empowerment Inventory, the Patient-Doctor Relationship Questionnaire, the Chao continuity questionnaire, and the Southgate Institute for Health Society and Equity for their generosity in sharing their work with the authors [ 17 , 19 - 25 ]. This study would not have been possible without the support of the administrative team of the Academic Unit of General Practice. This project was funded by the Royal Australian College of General Practitioners (RACGP) through the RACGP Foundation IPN Medical Centres Grant, and the authors gratefully acknowledge their support.

Data Availability

The data sets generated during this study are not publicly available due to the nature of our original ethics approval but are available from the corresponding author on reasonable request.

Authors' Contributions

MAC acquired the funding, conceptualized the project, and organized interview recruitment. MAC and KB conducted interviews and analyzed the qualitative data. EAS, ER, and KD contributed to project planning, supervision and qualitative data analysis. MAC, KB and KO wrote the survey and planned quantitative data analysis. MAC and KB recruited practices for survey administration. KO and KB conducted the quantitative data analysis. MAC and KO, with KB drafted the paper. EAS, ER, and KD helped with reviewing and editing the paper.

Conflicts of Interest

None declared.

Phase 1 interview guide.

Phase 2 survey: eHealth and digital divide.

Phase 2 participant characteristics by frequency of eHealth use.

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Abbreviations

Edited by T Leung; submitted 03.07.23; peer-reviewed by T Freeman, H Shen; comments to author 16.08.23; revised version received 30.11.23; accepted 31.01.24; published 11.04.24.

©Melinda Ada Choy, Kathleen O'Brien, Katelyn Barnes, Elizabeth Ann Sturgiss, Elizabeth Rieger, Kirsty Douglas. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

• Open access
• Published: 09 April 2024

Flipped online teaching of histology and embryology with design thinking: design, practice and reflection

• Yan Guo 1 ,
• Xiaomei Wang 1 ,
• Yang Gao 1 ,
• Haiyan Yin 1 ,
• Qun Ma 1 &
• Ting Chen 2  

BMC Medical Education volume  24 , Article number:  388 ( 2024 ) Cite this article

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Flexible hybrid teaching has become the new normal of basic medical education in the postepidemic era. Identifying ways to improve the quality of curriculum teaching and achieve high-level talent training is a complex problem that urgently needs to be solved. Over the course of the past several semesters, the research team has integrated design thinking (DT) into undergraduate teaching to identify, redesign and solve complex problems in achieving curriculum teaching and professional talent training objectives.

This study is an observational research. A total of 156 undergraduate stomatology students from Jining Medical University in 2021 were selected to participate in two rounds of online flipped teaching using the design thinking EDIPT (empathy, definition, idea, prototype, and test) method. This approach was applied specifically to the chapters on the respiratory system and female reproductive system. Data collection included student questionnaires, teacher-student interviews, and exam scores. GraphPad Prism software was used for data analysis, and the statistical method was conducted by multiple or unpaired t test.

According to the questionnaire results, the flipped classroom teaching design developed using design thinking methods received strong support from the majority of students, with nearly 80% of students providing feedback that they developed multiple abilities during the study process. The interview results indicated that teachers generally believed that using design thinking methods to understand students' real needs, define teaching problems, and devise instructional design solutions, along with testing and promptly adjusting the effectiveness through teaching practices, played a highly positive role in improving teaching and student learning outcomes. A comparison of exam scores showed a significant improvement in the exam scores of the class of 2021 stomatology students in the flipped teaching chapters compared to the class of 2020 stomatology students, and this difference was statistically significant. However, due to the limitation of the experimental chapter scope, there was no significant difference in the overall course grades.

The study explores the application of design thinking in histology and embryology teaching, revealing its positive impact on innovative teaching strategies and students' learning experience in medical education. Online flipped teaching, developed through design thinking, proves to be an effective and flexible method that enhances student engagement and fosters autonomous learning abilities.

Peer Review reports

Research background and motivation

Histology is the study of the microstructure and related functions of the human body [ 1 ], while embryology studies the laws and mechanisms of ontogenesis and development; these two sciences are interrelated and self-contained [ 2 ]. As one of the important professional core programs of most medical specialties, Histology and Embryology (HE) has been an indispensable curriculum bridge between normal microstructure and pathological changes in tissue and organs.

The teaching targets of HE are mainly first-year undergraduate students in clinical medicine, psychiatry, stomatology, nursing, etc. The importance of fostering the development of empathy in undergraduate students is continuously emphasized in international recommendations for medical education [ 3 ]. Freshmen have a certain ability to think logically and analyse problems, but this ability is limited, and they have a yet to develop familiarity with scientific research hotspots. Moreover, they are often unaware of their creative potential, and this phenomenon often causes them to passively accept knowledge, and their autonomous learning ability and student participation in class are less than that of upperclassmen. These first year students face the need to develop scientific literacy and the ability to integrate theory with practice [ 4 ]. However, traditional teaching methods may have failed to fully meet students' need for a profound understanding of these abstract concepts, leading to challenges such as low interest in learning and inadequate knowledge absorption. Consequently, educators urgently need to seek innovative teaching strategies to enhance students' learning experience and academic performance.

In the information age, teacher teaching is no longer a simple superposition of knowledge and teaching methods but a fusion innovation of technology and teaching oriented to a more complex learning environment. The Teacher Standards issued by the American Educational Technology International Association note that the important role of future teachers is that of a "designer" [ 5 ]. DT combines a creative and innovative approach to dealing with complex problems, which serves as a valuable tool for those seeking to improve the challenging issues in medical education [ 6 ]. DT is a process of analysis that relies on the deconstruction of ideas and a creative process that relies on the construction of ideas. There are no judgements in DT. This eliminates the fear of failure and encourages maximum input and participation. Wild ideas are welcome since they often lead to the most creative solutions. Everyone is a designer, and DT is a way to apply design methodologies to any situation [ 5 ].

In the field of education, DT has been advocated as a means to promote the cultivation of innovative talent through innovative teaching methods. With the help of DT, and adhering learning as the concept in teaching, the transformation of teaching allows learners to explore real needs in real life scenes, to propose innovative solutions to meet those needs through teamwork, and to test the effectiveness of those solutions through prototype production. This process facilitates the further application of constructivism [ 7 ].

In the process of both conventional teaching and teaching innovation, the research team utilizes the “EDIPT” (Empathy, Define, Ideate, Prototype and TEST) DT theory [ 8 ] which originating in the Stanford University Design School to design teacher activities and student activities and select technical tools [ 9 ]. The basic process is shown in Fig.  1 . The team is very accustomed to consciously applying DT methodology when facing difficulties and challenges to consistently obtain the desired results [ 10 ]. This study sets the teaching objectives and plans of a large cycle (one semester) to guide the teaching implementation of a small cycle (one section or one chapter); Then, small-cycle teaching feedback and achievement accumulation promote the progress of large-cycle teaching to ensure the coherence, effectiveness and improvement of teaching reform. For example, the difficult problem in the process of cardiovascular system embryogenesis is atrial separation; the team uses cardboard and plastic film to construct room partition "products" [ 11 ] to provide vivid explanations and body movements for clearer explication. In another example, they integrate scattered knowledge points including cleavage, blastocyst formation and implantation into a unified narrative called "the initial journey." It solves the pain point that the dynamic abstraction of embryology knowledge is difficult to intuitively understand. The above are two examples of using EDIPT steps of design thinking to solve teaching pain points.

Problem solving steps incorporating DT

Research objectives and significance

In the 2021 Horizon Report: Teaching and Learning Edition, blended learning was once again selected as the key technology affecting the future development and practice of higher education [ 12 ], demonstrating great application potential. In this format, the teaching team adheres to the following practical principles to promote more blended learning courses to ensure high-quality outcomes [ 13 ]. In the recent period of epidemic prevention and control, effective online teaching combines asynchronous and synchronous delivery modes, addresses knowledge learning and ability development, and highlights interaction in teaching activities to improve the online teaching experience for both teachers and students and enhance the overall quality of online teaching. Online teaching is not simply an emergency measure taken during the epidemic but rather represents the future trend of education.

The aim of this study is to explore the application of design thinking in the teaching of histology and embryology courses. By investigating the impact of design thinking in the teaching process, we aim to gain a deeper understanding of the effects of this innovative teaching strategy on students' learning experience and academic performance, as well as its potential applications in medical education.

The significance of this research lies in its contribution to medical education with novel teaching methods and strategies. By incorporating design thinking, educators can better cater to students' learning needs and enhance their comprehension and mastery of the subject matter. Furthermore, this study contributes to the expansion of teaching research in the field of medical education, providing valuable insights for educational reform and improvements in teaching quality.

The analysis of the correlation between design thinking and this study

Design thinking plays a crucial role in formulating the educational reform. During the empathize phase, an in-depth understanding of teachers' and students' needs and challenges is achieved. This includes considering teachers' expectations and pedagogical beliefs, as well as students' learning styles and feedback, leading to a clear definition of the problem and setting specific objectives for the educational reform. In the define phase, the importance of improving teachers' pedagogical approaches and methods, and cultivating students' creative learning and competencies is underscored. This serves as the foundation for selecting appropriate teaching strategies and establishes the specific direction for incorporating design thinking in the flipped classroom model. During the ideate phase, innovative thinking is employed to explore diverse teaching strategies. For enhancing teachers' pedagogical approaches, approaches such as case-based teaching and collaborative learning are recommended to stimulate students' intrinsic motivation for active learning. For promoting students' creative learning and overall competencies, methods like project-based learning and critical thinking cultivation are considered to facilitate holistic student development. In the prototype phase, the devised teaching strategies are implemented in the flipped classroom setting. Continuous prototyping and rapid experimentation facilitate the collection of valuable feedback and data from students and teachers, enabling further optimization of the teaching strategies to align with the original intent of design thinking. Finally, in the test phase, a comprehensive evaluation of the teaching implementation is conducted. By collecting and analyzing data, the study delves deep into the impact of the educational reform on teachers' pedagogical beliefs and students' creative learning and overall competencies. This process provides crucial feedback and evidence for the ongoing improvement of the educational reform.

In conclusion, the selection of flipped classroom as a pedagogical strategy is closely guided by design thinking principles. Through the application of design thinking, this observational study aims to enhance teachers' pedagogical approaches and methods while fostering students' creative learning and overall competencies, thus promoting the successful implementation of the educational reform.”

Flipped classroom sessions can also allow learners to gain competence through their educational endeavours [ 14 ]. As Bransford writes, “To develop competence in an area, students must: a) have a deep foundation of factual knowledge, b) understand facts and ideas in the context of a conceptual framework, and c) organize knowledge in ways that facilitate retrieval and application” [ 15 ]. Flipped classrooms can lead to competence in factual knowledge by fostering mastery of content through content understanding and application, as in traditional classrooms [ 16 ].

“O-PIRAS” Flipped classroom

The flipped classroom teaching model used in this study was formed and adjusted on the basis of Professor Jianpeng Guo's “O-PIRTAS” model. The flipped teaching mode can enable both teachers and students to acquire further abilities through teaching activities [ 17 ].

The first step(O: Objective) in flipped classroom teaching design is to formulate two types of teaching objectives: low level and high level. The lower level teaching objectives include two cognitive objectives from Bloom's classification of teaching objectives: the memory and understanding of knowledge, while the higher level teaching objectives include four cognitive objectives from Bloom's classification: application, analysis, evaluation and creation, as well as objectives pertaining to movement skills and emotion [ 18 ]. The second step is to design a preparation activity (P: Preparation) for students to complete before class, which helps students form necessary prior knowledge and stimulates their learning motivation by exploring relevant issues prior to the class [ 19 ]. The third step is for teachers to send teaching materials (I: Instructional video) to their students for pre-class learning to facilitate their early acquisition of knowledge [ 19 ]. Fourth, teaching is transferred from online classes to offline classes. The teacher briefly reviews (R: Review) the video content before class to help students quickly focus on and prepare for the next stage of learning both cognitively and psychologically. Fifth, teachers should design classroom activities (A: Activity) appropriate to high-level teaching objectives to promote in-depth learning and successfully achieve high-level objectives. Sixth, teachers should conduct classroom summaries (S: Summary), reflection and improvement to help students form integrated structured knowledge. The six steps of flipping the classroom link form a closed loop, which can be summarized as in Fig.  2 .

Process of “O-PIRAS” flipped teaching

Research method and data collection

Conveniently selecting 156 undergraduate students majoring in Dentistry from the 2021 cohort of Jining Medical University, we designated classes 1 to 3 as the class of 2021 stomatology students. As the class of 2020 stomatology students, we chose 155 undergraduate students majoring in Dentistry from the 2020 cohort, also from classes 1 to 3. Prior to the start of the study, we conducted communication sessions with both teachers and students, ensuring that all students were well-informed about the study and provided their consent. The two groups of students had the same course hours, faculty resources, learning materials, and learning spaces. The only difference was the application of design thinking methods in course and teaching design, including the implementation of flipped classroom teaching, specifically tailored for the 2021 cohort of students.

Data collection was conducted through various methods, including distributing questionnaires, conducting pre-, mid-, and post-research interviews, and recording course and corresponding chapter test scores. The implementation chapter selected the respiratory system, which plays a bridging role within histology, and the female reproductive system, which plays a transitional role between histology and embryology.

Before studying "Respiratory System", students have already mastered the basic methods of using design thinking to learn histology, and have a deep understanding of the four basic tissues and two types of organs (hollow and substantial organs). The main organs of the respiratory system—the trachea and lungs—belong to two types, respectively. The female reproductive system, as the concluding chapter of histology, is separated from the flipped classroom of the respiratory system by two weeks, leaving appropriate time for teachers to iteratively design and students to adapt to new methods. Four surveys were administered during the research process: Pre-flipped classroom survey for Chapter 16 "Respiratory System", Post-flipped classroom survey for Chapter 16 "Respiratory System", Pre-flipped classroom survey for Chapter 19 "Female Reproductive System", and Post-flipped classroom survey for Chapter 19 "Female Reproductive System", to gather student feedback and opinions on the teaching methods. The questionnaires were designed based on the research objectives and questions, and were refined through pre-testing to ensure clarity, accuracy, and appropriateness of the questions and options. The questionnaire mainly includes the following dimensions: ⑴Basic information of students, Q 1–3; ⑵ Learning and satisfaction: Q 4, What is the division of labor in your group in this cooperation? Q 7, About flipping class, how long will you spend studying before class? Q 6 Compared with the last flip class, are you satisfied with the teacher's teaching time in this flip class? Q 12, What are you most satisfied with this flip class? (3) Learning experience and ability improvement: Q 5, What kind of class learning form do you like best in flip class? Q 8, What are your learning pain points or difficulties after this flip class? Q 9, What abilities have you improved in this flip class? ⑷ Classroom Improvement and Feedback: Q 11, What are the advantages of this flip class compared with the last flip class? Q 10, In the course of embryo formation, do you like to use flip class for multiple course contents? Q13, What suggestions do you have for improving the embryo flipping class? Interviews were conducted at various stages, including before the study to understand teaching pain points, during the research process to gauge teachers' and students' attitudes and perspectives on the teaching activities, and after the study to obtain overall feedback. Additionally, we conducted both stage-specific and overall tests, and promptly collected relevant data for comparative analysis with the class of 2020 stomatology students. These data provided comprehensive insights into the performance and experiences of students in both the experimental and class of 2020 stomatology studentss.

Application of design thinking in course design

In course design, we employed design thinking methods to redesign the histology and embryology curriculum. Firstly, we gained a deep understanding of students' learning needs and interests to define course objectives and content. Secondly, we innovatively designed online materials and videos to enhance the appeal and practicality of the learning experience. We encouraged students to actively participate in discussions and problem-solving during class to unleash their creative potential. Additionally, we continuously optimized the teaching content and methods through iteration and feedback to ensure a sustained improvement in teaching effectiveness. Through the application of design thinking in course design, we expected to optimize the teaching process, enhance students' learning experiences, and improve their academic performance.

Design and implementation of flipped teaching

The HE course covers 22 chapters, totaling 60 h, including 44 h of theoretical classes and 16 h of practical classes. The theoretical teaching is roughly divided into three stages: the first stage consists of 12 h, focusing on introducing the four basic human tissues; the second stage comprises 18 h, covering the structure of human organs and systems; and the third stage spans 14 h, elucidating the process of human embryonic development. To facilitate a deep understanding and mastery of human tissue structures, four practical classes, each lasting 4 h, are incorporated to complement the theoretical content.

The entire course relies on a blended teaching approach, combining online and offline instruction, leveraging the resources of Shandong's top undergraduate course in HE, and utilizing the "Zhidao" flipped classroom tool. At the beginning of the course, the teachers introduce the purpose, teaching process, weekly plan, grading components, and assessment methods of incorporating design thinking into the blended HE teaching. The flipped classroom teaching for the class of 2021 stomatology students is set between two stage tests to investigate whether this innovative teaching method has an impact on students' test scores.

The teaching team consists of 4 associate professors and 3 lecturers, with an average teaching experience of 11.4 years in teaching nursing major foundation courses and possessing rich teaching experience. In addition, the project leader and team teachers have undergone multiple training sessions in design thinking innovation and systematic training in domestic and on-campus blended teaching theories.

At the beginning of the semester, the curriculum teaching plan should be formulated, and chapters suitable for flipped teaching should be selected according to the teaching plan” and content characteristics [ 20 ]. Teaching and research team members should jointly analyse the teaching content and formulate the flipped classroom syllabus [ 21 ], clarify teaching objectives (knowledge objectives, ability objectives and emotional objectives, i.e., low-order objectives and high-order objectives), develop chapter teaching plans and teaching courseware (traditional classrooms are obviously different from flipped classrooms) [ 22 ], record pre-class video (design the course content in a fragmented way and systematically present it in accordance with the teaching plan) [ 23 ], divide students into groups and engage with all students through “zhidao” teaching software and the QQ class committee. The specific design and implementation plan for the preparation of the above teaching materials for a flipped classroom course on the respiratory system. The teaching team seminar is held three weeks before the class.

While completing the preparation of teaching materials in accordance with the teaching plan, the team clarified what methods and tasks teachers and students should complete before and during the implementation of the flipped classroom so that everyone can understand the design intent of these teaching activities to facilitate more satisfactory teaching results.

Practice processes and instructional evaluation

The teaching design was discussed and approved by all members of the research team and used in the classroom teaching of respiratory system conversion with slightly modified specific content. One week before class, it was distributed through the zhizhuishu teaching platform to all the students [ 24 ] participating in flipped classroom teaching. The resources provided to students include preview materials, textbook chapters, courseware, videos, etc.; Preview questions, some questions related to preview materials, guide students to think and explore, stimulate learning interest and initiative; Learning objectives, clarify the knowledge objectives, ability objectives, and literacy objectives for pre class learning. In addition, there are also learning platforms (Wisdom Tree Online Course- https://coursehome.zhihuishu.com/courseHome/1000007885/199185/20#onlineCourse ),WeChat class group chat, learning community. In flipping the method of respiratory system class delivery, the team first tried to perform a complete flip of the class. At the beginning of the class, the teacher clarified six themes, and then the group spokespersons demonstrated their understanding of all the knowledge points, including key points and difficulties, in class by drawing lots. The teams provided feedback for each other. The teacher only played a guiding role in the activities involving the entirety of the class. After summarizing the classroom content, the teacher assigned homework, such as creating mind maps and engaging in thematic discussions on the learning platform, and distributed the questionnaire regarding the group pre-class preparations, classroom activities and learning experiences for the respiratory system flipped classroom. The questionnaire mainly consists of the following questions. How was the work divided among your team for this activity? What kind of in-class learning style do you like best in the flipped classroom? Compared with the last flipped classroom, are you satisfied with the length of teaching in this flipped classroom? How long do you spend on pre-class learning for a flipped class? What are your learning pain points or difficulties after this flipped lesson? What abilities have you improved in this flipped classroom? Are you satisfied with the length of lectures in this class compared with that in the last flipped class? What percentage of the course content do you prefer to be delivered by the flipped classroom model? Compared with the last flipped classroom, what are the advantages of this flipped classroom? What you are most satisfied with in this flipped lesson? Please offer suggestions for the improvement of your flipped class on embryos.

According to the steps and links involved in DT, when the “product” (teaching plan) is tested and problems are found, the design team should complete the iteration as soon as possible to better meet the needs of “customers” (students)[ 25 , 26 ]. Three days after the questionnaires, the teaching team adjusted the flipped classroom teaching design scheme for the Female Reproductive System course according to the questionnaire results, and arranged the pre-class tasks one week prior to the class, which differed from the previous class. Explanations of key points and difficult points were appropriately added to the teaching design, which did not depend on students as thoroughly as it had the last time, reducing the difficulty of the flipped classroom to a certain extent, improving students' level in participation, and improving the learning effect and teaching quality of the class.

A total of four questionnaires were distributed before and after the two flipped classes, and video recordings were made of the flipped classroom teaching process for a nursing and a stomatology class. Tencent conference recording instructions were issued by teachers. HE course scores consisted of three parts, including the usual score (30%), experimental score (10%) and final score (60%). The course scores of the 2021 nursing class and stomatology class were derived from the education management system of Jining Medical College, and the course scores of the nursing and stomatology majors who did not classes that had implemented online flipped classroom teaching in 2020 were derived as a control. Comparing the proportion of students in each of two grades, the total correct response rate of test questions, and the correct response rate of respiratory system and female reproductive system course test questions delivered through flipped classroom teaching were analysed using GraphPad Prism software through the statistical method of multiple or unpaired t tests.

Teaching strategies developed using design thinking methods improves multiple student abilities

According to the results of the questionnaire distributed before the beginning of the first flipped class, 51.2% of the students reported not understanding the new learning method and that they could not check the data, 21.6% of the students were not interested in flipped lessons and preferred traditional passive learning methods, 25.6% of the students said that they did not have strong self-control and were unwilling to take the initiative to learn, 56.8% of the students said that they had a great fear of speaking in front of their classmates and that their public speaking skills were not strong, and 46.4% of the students did not know how make suitable PowerPoint Presentation (PPT). After two sessions of flipped classroom learning, the majority of students felt that their pain points had been effectively solved and various abilities had been developed. The results of the question after the flipped classroom teaching of the female reproductive system are shown in Table  1 .

Positive feedback and growth experience of students in teaching strategies developed using design thinking methods

The informal discussion following the flipped lesson on the female reproductive system shows that compared with the "Teacher almost let go" response in the previous respiratory system flipping class, the students are more inclined to respond with "The teacher will solve the problems left in our preview," "Feedback is provided between groups, and the groups are complementary," "The teacher emphasizes the key points, explains the process in detail, and plays videos to consolidate knowledge," and " the teacher commented on the performance of the group speaker". The students thought that after two sessions of participation in a flipped classroom, "We are more active in learning and the classroom design is more live," and "The students are more involved and confident." "By applying design thinking to study the course of organizational design, I have found new learning methods and approaches, and successfully applied these learning methods to other courses, which has benefited me greatly."

The comparison results of grades

Under the premise that there is no significant difference in the difficulty of test questions and other criteria between the flipped and traditional classrooms, the class of 2021 stomatology students' course scores showed a slight improvement. However, there was no significant difference in the distribution of the number of students in each score segment compared to the class of 2020 stomatology students. In contrast, for the chapters that implemented flipped classroom teaching, specifically the respiratory system and female reproductive system chapters, the class of 2021 stomatology students' test scores showed a significant increase. The difference between the two groups was statistically significant. The details are depicted in Fig.  3 .

Distribution of final exam scores for the two graduating classes. A The proportion of students in different grades, no significant difference Statistical method: Multiple t tests. B Total accuracy, no significant difference. Statistical method: Unpaired test. C The accuracy of flipped classroom chapters, unpaired test, P  < 0.05. Mean ± SEM of column A 0.7075 ± 0.009587 N  = 3, Mean ± SEM of column B 0.7913 ± 0.02872 N  = 3

Firstly, significant achievements have been made in enhancing students' overall abilities through the application of design thinking methods in formulating flipped classroom teaching strategies. Preliminary surveys revealed various challenges faced by students before the commencement of the flipped classes, including difficulties in understanding new learning methods, lack of interest in flipped classes, low self-discipline, and fear of public speaking. However, after two sessions of flipped classroom learning, the majority of students believe that their pain points have been effectively addressed, and various skills have been developed. This aligns with the findings of previous research by Awan OA [ 15 ], indicating that the application of design thinking methods in teaching strategies can significantly enhance students' subject engagement and skill development.

Secondly, regarding the positive feedback and students' growth experiences in applying design thinking methods to formulate teaching strategies, there is a positive trend observed in informal discussions following the flipped classroom on the female reproductive system. Students tend to perceive a more proactive role played by teachers in the flipped classroom, addressing the issues they encountered during previewing. Students also highlighted the complementary feedback provided among groups, emphasizing the importance of teamwork. Additionally, students positively acknowledged the efforts of teachers in emphasizing key points, providing detailed explanations of processes, and reinforcing knowledge through video presentations. They believe that this teaching approach stimulates their interest in learning and enhances their motivation. This aligns with the findings of research by Scheer A [ 7 ] and Deitte LA [ 11 ], supporting the positive impact of design thinking methods in education.

Finally, the results of the performance comparison indicate that there is no significant difference between flipped classroom and traditional classroom based on criteria such as question difficulty. However, the overall grades of the 2021 cohort of dental medicine students have shown a slight improvement. Specifically, in the chapters on the respiratory and female reproductive systems within the flipped courses, the exam scores of the 2021 cohort students have significantly increased, and this difference is statistically significant. This suggests that the flipped classroom teaching formulated through design thinking methods has a significant positive impact on the development of subject-specific skills in specific chapters. This aligns with the relevant findings of Cheng X [ 1 ], further emphasizing the instructional advantages of design thinking methods in specific topics.

Main finding

The team used DT to reveal the pain point that flexible mixed teaching can not guarantee students' participation and the realization of teaching objectives, and the application of online flip classroom teaching solved this problem well Students play a leading role in this kind of teaching, so they need to devote more time and energy to preview textbooks and consult relevant materials before class to improve their autonomous learning ability It is helpful to cultivate team spirit in flip teaching in the form of group, which is helpful to cultivate team leadership and management ability. The main requirements of mixed teaching are to integrate pre-recorded videos into the course as a whole and provide online learning resources to supplement face-to-face teaching in an organized and selective way [ 27 ] As assessment expert Mag says, if you are teaching something that cannot be assessed, you are already in an awkward position-that is, you can't explain the teaching content clearly [ 28 ] Therefore, reasonable teaching objectives in mixed teaching can make teachers and students reach a common understanding and consensus on learning results, enhance emotional communication and resonance between teachers and students, and jointly promote the implementation of curriculum teaching. The successful implementation of online flip class needs certain network and students' enthusiasm and cooperation At the same time, teachers need to be particularly familiar with the curriculum to design lectures and targeted comments [ 29 ].

Limitations and future research

In this study, the respiratory system and female reproductive system in HE were selected as subjects for conducting flipped classroom teaching. The examination results shoe that although the overall course performance has not significantly improved, the accuracy of the chapter test questions in flipped classrooms significantly improved, which demonstrates that this teaching method can improve students' learning performance while cultivating their various abilities. It is worth expanding the scope of implementation to more chapters. However, not all chapters are suitable for flipped classroom teaching. Because the two chapters involved in this paper belong to the "organs and systems" module, it does not fully reflect the applicability of this research in this course. Some chapters of the basic tissue module and embryogenesis module are also the scope of our future teaching research In addition, what is the highest proportion of total course hours converted to flip teaching? All these problems need further study in the future. What is the most appropriate ratio of total course hours to convert into flipped teaching? These issues need to be further studied in the future.

When DT is introduced into education, evaluating students' learning and development becomes more important than evaluating students' design products or knowledge and ability. Changes in consciousness and attitude include whether they can fully participate in current cognitive activities, learn independently, communicate and cooperate, and continuously monitor and adjust themselves. By clarifying this guidance, we can formulate or select appropriate evaluation criteria through a literature review during the implementation of the project and adjust the subsequent research conditions in a timely manner according to the evaluation results.

Online flipped teaching is an effective way to integrate DT into the flexible and mixed teaching of HE, which can effectively enhance students' learning input and cultivate students' autonomous learning ability. This research aims to reshape the method of classroom teaching through the deep integration of modern information technology into pedagogical design. Future work should appropriately expand the scope of flipped teaching content and explore the appropriate proportion of course content. In the course design, various forms of cross-professional cooperation with clinical doctors should be increased as much as possible, and the contents of flipped classroom should be expanded from basic knowledge to clinical skills.

Through the application of design thinking in the teaching of histology and embryology courses, we have gained a deeper understanding of its positive impact on innovative teaching strategies, improvement of students' learning experience and academic performance, and the potential value it holds in medical education. We have discovered that the "product" developed through design thinking, namely online flipped teaching, serves as an effective and flexible blended teaching method. It not only enhances students' engagement in learning and fosters their autonomous learning abilities but also encourages both teachers and students to cultivate their innovative capabilities and reshape classroom teaching approaches. Moving forward, further exploration should be undertaken to determine the optimal balance for expanding the content of flipped teaching, to continually uncover its potential in medical education.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

Abbreviations

Histology and Embryology

Design Thinking

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Acknowledgements

We thank Jining Medical College and Shandong Provincial Education Department for support.

Undergraduate Teaching Reform Research Project of Shandong Provincial Education Department No. M2021364, M2022159. Research on classroom teaching reform key program in Jining Medical College [2022] No. 2022KT001.

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Yan G, Ting C: Conceptualization, Methodology, Writing-original draft preparation. Xiaomei W, Yang G: Interview, Data curation, Formal Analysis. Haiyan Y, Qun M: Formal analysis, Writing-reviewing and editing. The author(s) read and approved the final manuscript.

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Guo, Y., Wang, X., Gao, Y. et al. Flipped online teaching of histology and embryology with design thinking: design, practice and reflection. BMC Med Educ 24 , 388 (2024). https://doi.org/10.1186/s12909-024-05373-7

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Single-shot measurement of photonic topological invariant

Nathan roberts, guido baardink, anton souslov, and peter j. mosley, phys. rev. research 6 , l022010 – published 11 april 2024.

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Topological design enables robustness to be engineered into a system. However, a general challenge remains to experimentally characterize topological properties. In this work, we demonstrate a technique for directly observing a winding-number invariant using a single measurement. By propagating light with a sufficiently broad spectrum along a topological photonic crystal fiber, we calculate the winding number invariant from the output intensity pattern. We quantify the capabilities of this single-shot method, which works even for surprisingly narrow and asymmetric spectral distributions. We demonstrate our approach using topological fiber, but our method is generalizable to other platforms. Our method is experimentally straightforward: we use only a broadband input excitation and a single output to measure the topological invariant.

• Received 19 July 2023
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DOI: https://doi.org/10.1103/PhysRevResearch.6.L022010

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• 1 Department of Physics, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
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Schematic of the single-shot measurement. (a) Cross section of the 12-core topological photonic crystal fiber based on the SSH chain (left). The different air hole sizes (see right zoom) give rise to alternating weak-strong intercore couplings. (b) A narrow spectrum of light is coupled into a bulk core (left). The resultant intensity profile (right) is used to calculate a weighted intensity difference I d , which is then wavelength averaged to measure the system's topological invariant. (c) Our method uses a broadband spectrum, allowing the invariant to be calculated in a single shot.

(a) Heuristic explanation of the connection between the output intensity profile and the topological invariant. A broad spectrum excites a single core and the output intensity profiles are considered for two extreme cases, C 1 = 0 and C 2 = 0 . For the topologically trivial case C 2 = 0 , light stays within a single unit cell and the average intensity difference is zero. For the nontrivial case C 1 = 0 , light cannot couple to the other core within the same unit cell. On average, half the light intensity ends up in the neighboring unit cell, making the weighted intensity difference 2 〈 I d 〉 λ = 1 . (b) Schematic explanation of our experiment. The intensity distributions per unit wavelength for both the narrowest spectrum (purple) and the widest spectrum (dashed teal) are shown in the plot. The intensity distributions are used to excite core six of the topological fiber before the output is imaged onto a camera. The two intensity plots shown correspond to the two spectra shown in the plot. (c) Experimental data showing the effects of changing the spectral width on the winding-number measurement ( ν ). The black crosses are experimental averages of three measurements, with the error bars being their standard deviation. The observed winding numbers stay around the expected value of one, but the uncertainty associated with the measurements (gray shaded region) grows as the root mean square (RMS) spectral width decreases. The green diamonds and red triangles show the theoretical predictions of our measurement when the experimental spectra are propagated. The diamonds correspond to the system's topological state with the same couplings as in our experiment, while for the triangles, the C 1 and C 2 couplings are flipped, leaving the system in a topologically trivial phase.

(a) Four example distributions of the input spectrum. We vary the root mean square (RMS) width of the input spectrum from 27.8 nm (turquoise) to 5.8 nm (dark green) by reducing the standard deviation of the distribution. (b) shows the calculated winding number ( ν ) for each of these input spectra as a function of the RMS width of the distribution. (c) Response of the weighted intensity difference to changing wavelength (red) and changing distance (blue) in the topologically nontrivial case. Both plots show twice the weighted intensity difference oscillating around one, the expected value of the winding number that characterizes the system. (d) Winding numbers calculated by averaging the wavelength and distance curves plotted in (c). (e) Product of the distribution density and 2 I d [which approaches the winding number as shown in Eq. ( 2 )], for the distributions shown in (a). We show graphically that the mean of this function becomes closer to the winding number, ν = 1 as the RMS width of the exciting spectrum increases.

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AI makes retinal imaging 100 times faster, compared to manual method

Researchers at the National Institutes of Health applied artificial intelligence (AI) to a technique that produces high-resolution images of cells in the eye. They report that with AI, imaging is 100 times faster and improves image contrast 3.5-fold. The advance, they say, will provide researchers with a better tool to evaluate age-related macular degeneration (AMD) and other retinal diseases. 

Vineeta Das, NEI Clinical and Translational Imaging Section, explains how artificial intelligence improves imaging of the eye’s light-sensing retina.

“Artificial intelligence helps overcome a key limitation of imaging cells in the retina, which is time,” said Johnny Tam, Ph.D., who leads the Clinical and Translational Imaging Section at NIH's National Eye Institute.

Tam is developing a technology called adaptive optics (AO) to improve imaging devices based on optical coherence tomography (OCT). Like ultrasound, OCT is noninvasive, quick, painless, and standard equipment in most eye clinics. 

Image of a commercially available OCT device, used to image intraocular tissues such as the light-sensing retina. Credit: Adobe Stock.

“Adaptive optics takes OCT-based imaging to the next level,” said Tam. “It’s like moving from a balcony seat to a front row seat to image the retina. With AO, we can reveal 3D retinal structures at cellular-scale resolution, enabling us to zoom in on very early signs of disease.” 

Johnny Tam, Ph.D., NEI Clinical and Translational Imaging Section. 

While adding AO to OCT provides a much better view of cells, processing AO-OCT images after they’ve been captured takes much longer than OCT without AO. 

Tam’s latest work targets the retinal pigment epithelium (RPE), a layer of tissue behind the light-sensing retina that supports the metabolically active retinal neurons, including the photoreceptors. The retina lines the back of the eye and captures, processes, and converts the light that enters the front of the eye into signals that it then transmits through the optic nerve to the brain. Scientists are interested in the RPE because many diseases of the retina occur when the RPE breaks down. 

Illustration of the eye showing the location of the retina and its retinal pigment epithelium (RPE). 

A top-down view of lab-grown RPE cells as seen with high-resolution microscopy. Unlike AO-OCT, which is performed in an awake person, this image was created with preserved tissue. Credit: Kapil Bharti, National Eye Institute. 

Imaging RPE cells with AO-OCT comes with new challenges, including a phenomenon called speckle. Speckle interferes with AO-OCT the way clouds interfere with aerial photography. At any given moment, parts of the image may be obscured.   Managing speckle is somewhat similar to managing cloud cover. Researchers repeatedly image cells over a long period of time. As time passes, the speckle shifts, which allows different parts of the cells to become visible. The scientists then undertake the laborious and time-consuming task of piecing together many images to create an image of the RPE cells that's speckle-free. 

Tam and his team developed a novel AI-based method called parallel discriminator generative adversarial network (P-GAN)—a deep learning algorithm. By feeding the P-GAN network nearly 6,000 manually analyzed AO-OCT-acquired images of human RPE, each paired with its corresponding speckled original, the team trained the network to identify and recover speckle-obscured cellular features.  

When tested on new images, P-GAN successfully de-speckled the RPE images, recovering cellular details. With one image capture, it generated results comparable to the manual method, which required the acquisition and averaging of 120 images. With a variety of objective performance metrics that assess things like cell shape and structure, P-GAN outperformed other AI techniques. Vineeta Das, Ph.D., a postdoctoral fellow in the Clinical and Translational Imaging Section at NEI, estimates that P-GAN reduced imaging acquisition and processing time by about 100-fold. P-GAN also yielded greater contrast, about 3.5 greater than before. 

(A) A top-down view of the RPE layer as seen by clinical OCT. Although the image is zoomed in to the scale of single cells, it is difficult to visualize the cells. (B) AO-OCT provides a more detailed image of the RPE layer, but the cells are obscured by speckle. (C) There is a remarkable improvement in RPE cell visualization gained by applying AI to the speckled AO-OCT image. Each dark area represents a single RPE cell. Credit: Vineeta Das, National Eye Institute.

By integrating AI with AO-OCT, Tam believes that a major obstacle for routine clinical imaging using AO-OCT has been overcome, especially for diseases that affect the RPE, which has traditionally been difficult to image.

“Our results suggest that AI can fundamentally change how images are captured,” said Tam. “Our P-GAN artificial intelligence will make AO imaging more accessible for routine clinical applications and for studies aimed at understanding the structure, function, and pathophysiology of blinding retinal diseases. Thinking about AI as a part of the overall imaging system, as opposed to a tool that is only applied after images have been captured, is a paradigm shift for the field of AI.”

More news from the NEI Clinical and Translational Imaging Section .

This press release describes a basic research finding. Basic research increases our understanding of human behavior and biology, which is foundational to advancing new and better ways to prevent, diagnose, and treat disease. Science is an unpredictable and incremental process— each research advance builds on past discoveries, often in unexpected ways. Most clinical advances would not be possible without the knowledge of fundamental basic research. To learn more about basic research, visit https://www.nih.gov/news-events/basic-research-digital-media-kit .

NEI leads the federal government’s efforts to eliminate vision loss and improve quality of life through vision research…driving innovation, fostering collaboration, expanding the vision workforce, and educating the public and key stakeholders. NEI supports basic and clinical science programs to develop sight-saving treatments and to broaden opportunities for people with vision impairment. For more information, visit   https://www.nei.nih.gov .  

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit https://www.nih.gov/. 

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Office of the Vice President for Research

Photo recap: spring undergraduate research festival showcases students working in a range of disciplines.

The smell of popcorn filled the air as undergraduates lined the walkways of the University Capitol Center, showcasing their research and scholarship for campus and community members at the 20 th annual Spring Undergraduate Research Festival on Wednesday, April 3.

The event, hosted by the Office of Undergraduate Research (OUR), featured nearly 150 student researchers presenting work ranging from creating virtual reality performances of Shakespeare to investigating novel drug delivery methods to treat bladder cancer. Volunteer evaluators, recruited from campus and community, chatted with students about their work and provided valuable feedback. The festival also included a new exhibit hall, which provided space for students with three-dimensional objects as part of their portfolio of work.

“Our undergraduate research festivals provide students a valuable opportunity to practice communicating their scholarship with audiences unfamiliar with their chosen discipline,” said Bob Kirby, OUR director. “These events also give our campus community an opportunity to find inspiration in the ingenuity and work ethic of these students.” He added that the festival was largest ever in terms of student presenters and attendees. 

One in three undergraduates participates in research by the time that they graduate from the University of Iowa. The Spring Undergraduate Research Festival program of presenters and program of abstracts are available on the OUR website.   OUR, a unit of the Office of the Vice President for Research, hosts two undergraduate research festivals each year, one in fall and one in spring.