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The Application of Content Analysis in Nursing Science Research pp 13–21 Cite as

Inductive Content Analysis

  • Helvi Kyngäs 4  
  • First Online: 01 November 2019

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This chapter explains how to perform inductive content analysis, a method that is commonly used in qualitative studies to analyse data. This method can be applied to open or half-structured data. Inductive content analysis utilises the process of abstraction to reduce and group data so that researchers can answer the study questions using concepts, categories or themes. After a unit of analysis has been chosen, the researcher goes through the data to identify open codes, which are then combined with other open codes that include similar content to form sub-concepts, -categories and -themes. As in the previous step, these sub-concepts, -categories and -themes are combined into concepts, categories and themes, which can still further be organised into main concepts, categories and themes. The identified concepts, categories and themes (or main concepts, categories and themes) will serve as the basis for reporting content analysis results. This type of analysis is data-sensitive; hence, the researcher should return to the original data several times during the analytical process to ensure that the results show a strong connection to the analysed data.

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Kyngäs, H. (2020). Inductive Content Analysis. In: Kyngäs, H., Mikkonen, K., Kääriäinen, M. (eds) The Application of Content Analysis in Nursing Science Research. Springer, Cham.

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inductive analysis of data

The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

inductive analysis of data

  • Handling qualitative data
  • Transcripts
  • Field notes
  • Survey data and responses
  • Visual and audio data
  • Data organization
  • Data coding
  • Coding frame
  • Auto and smart coding
  • Organizing codes
  • Qualitative data analysis
  • Content analysis
  • Thematic analysis
  • Thematic analysis vs. content analysis
  • Narrative research
  • Phenomenological research
  • Discourse analysis
  • Grounded theory
  • Deductive reasoning
  • Introduction

Inductive logic

What is inductive reasoning, inductive vs. deductive reasoning, the inductive approach in the research process, further inquiry.

  • Qualitative data interpretation
  • Qualitative analysis software

Inductive reasoning and analysis

If you conduct research inductively, you derive a theory from your observations. A quantitative study can follow up an inductive analysis to substantiate an observation to generalize your theory to a population.

Inductive reasoning is an analytical approach that involves proposing a broader theory about the research topic based on the data that you use in your study. Inductive reasoning is a bottom-up approach where researchers construct knowledge and propose new theory that emerges from the data.

inductive analysis of data

Inductive and deductive reasoning goes hand in hand to allow researchers to develop a theoretical understanding of the human and social world. Let's look more closely at the concept of inductive reasoning and how it applies to research and in ATLAS.ti.

When people make specific observations about a particular phenomenon and draw conclusions based solely on the substance of those observations, they engage in a form of reasoning called inductive logic. Those conclusions can serve their working theory until other specific observations challenge or contradict their understanding.

They must then further develop their understanding into a more nuanced and coherent conclusion that accommodates their broadened observations of the world. Ultimately, the inductive method aims to construct a theory that explains relationships among the studied concepts or phenomena.

Inductive reasoning examples

Inductive reasoning becomes easier to understand as a bottom-up approach to logic. To take an example from everyday life, if one were to see a cat, notice that it has a tail, and come across other creatures that have tails, then they can reach a generalized conclusion through inductive reference based on their observations: all animals with a tail are cats.

Obviously, this does not mean that the proposed theory is the end of the inductive reasoning process. They can find a dog with a tail, but they would be hard-pressed to call it a cat.

As a result, the theory they have developed from previous experience could provide a better explanation. That person would have to conduct new observations of cats and dogs to make a further inductive inference: cats and dogs have tails, but cats have sharper claws. The cycle of inductive reasoning can thus continue indefinitely to identify patterns and develop more robust theories.

Another famous example is that of the black swan. You can inductively conclude that all swans are white if you have only observed white swans so far.

This theory must be thrown out when you encounter a black swan. Then you need to revise your theory to account for the new observation.

inductive analysis of data

The role of inductive reasoning in research is not always readily apparent if you only look at experimental research as a means for developing theory. Experimental research depends on deductive reasoning to confirm or dispute an existing theory, while inductive reasoning is most associated with observations and interviews.

Observation and inductive logic are most appropriate in research inquiries where the existing theory is not sufficiently developed or developed at all, requiring researchers to develop an inductive explanation about the phenomenon they are studying.

Especially in social science research, it's impossible to come to a necessarily final conclusion to the inductive reasoning process. Knowledge is always in constant development thanks to research.

Objective of inductive reasoning

The objectives of the inductive approach are to build theories from a set of data that allow researchers to make a general statement about a phenomenon while also opening up new lines of inquiry for future research.

It is also important to note that inductive research need not exist independent of existing theory. The research process always calls for connections to the existing literature to organize and generate knowledge. The main principle in applying inductive reasoning to your research is that the inferences you establish come from the data you analyze.

Is inductive analysis qualitative or quantitative?

Inductive reasoning is often associated with qualitative research , where the objective is to examine contexts, processes, or meanings that are not easily quantifiable. Quantitative analysis, on the other hand, tends to rely on deductive reasoning to test existing theories to suggest when established knowledge requires further development.

That said, inductive reasoning skills can be used with quantitative methods to form hypotheses based on the data . The important premise of an inductive approach is that propositions and theories are generated from the patterns of a phenomenon in a particular body of data.

Frequencies and themes

Patterns that occur in abundance across observations or interviews may be useful in developing theory. In addition, qualitative researchers may also identify patterns that appear only once or twice but that shed important light on the phenomenon under study.

ATLAS.ti, for example, has tools such as the Word Cloud to count the frequency of words. If you use a transcript of a speech, you can employ the Word Cloud tool and apply inductive reasoning to make a logical conclusion about a speaker's speech patterns based on the words they use most often.

inductive analysis of data

Deductive and inductive research are contrasting but complementary approaches used in scientific work. To clarify the difference, deductive approaches examine theoretical inferences from the top to bottom, while inductive methods aim to generate theoretical inferences from the bottom up. In other words, deductive reasoning works with current facts, while inductive reasoning seeks to create a new set of facts.

Looking at cats and dogs

To return to the example about cats and dogs, an example of a deductive inference would be one that uses an existing conclusion that all cats have tails and sharp claws. As a result, if someone finds an animal with a tail and sharp claws, they can employ deductive reasoning based on the above conclusion to call that animal a cat. Naturally, the more refined the theories employed, the more a researcher can rely on deductive reasoning.

The two approaches are not mutually exclusive and can be combined in the same scientific study. You can, for instance, build a code system starting with some deductively derived concepts, which you enrich throughout the analysis process with codes that you develop from the data inductively. In this sense, inductive and deductive reasoning both contribute to the analysis of your research.

You can use the Code Manager in ATLAS.ti to differentiate between the two sets of codes to organize inductive and deductive approaches in the same project. Colors and code groups can help you distinguish between the different kinds of codes you use to conduct your analysis.

For more complex research projects, smart codes can also facilitate the organization of your research by identifying segments of data that meet a certain set of criteria based on your codes.

inductive analysis of data

Organize projects large and small with ATLAS.ti

Our interface makes it easy to make sense of your research. See how with a free trial.

The research process can often be divided into data collection and data analysis . In qualitative research , coding is typically the intermediary step that facilitates analysis, moving you forward in developing conclusions and explaining them using theories.

Data collection

Inductive reasoning can be applied to most methods of data collection. That said, qualitative research methods that call for observations or interactions with research participants allow the researcher to employ inductive reasoning during data collection.

Imagine an interview project to determine the effects of social media usage. In initial interviews with people, the researcher may notice that many respondents mention physical effects like eye strain or lack of sleep. When the researcher believes there is a connection, they may adjust the questions they ask respondents to find more evidence of this causal relationship.

Similarly, with observations , a researcher employs inductive reasoning when they notice something that occurs frequently. For example, they might notice that people using smartphones in public tend to get in more accidents (e.g., bumping into others or tripping over objects). As a result, they can adjust their observations by going to crowded places where it is more likely people using smartphones might suffer more accidents.

An inductive reasoning approach to qualitative data analysis requires looking at your project to identify key segments of data that will ultimately serve as the premises for your development of theory. The theory can be further developed after identifying patterns and adjusting the focus to look for more evidence of or exceptions to those patterns.

inductive analysis of data

In ATLAS.ti, the process for employing an inductive approach starts with looking at your data. What patterns seem apparent? What shows up in the data? What instances of data appear most relevant to your research inquiry?

Give each pattern a short but descriptive label that forms one of your codes. Codes are short because they help summarize large segments for quick understanding or to categorize discrete segments in separate areas of your research project.

These codes can be created directly in the Code Manager, or you may find it easier to create codes while reading the data. As you read through your project, you can create new codes and then apply them to segments of data that are called quotations. Quotations given the same code can be said to be related to each other by the same broader pattern, thus establishing connections between different data segments with the same code.

As an example of this relationship, imagine you are coding a set of documents that contain people's schedules in everyday life. These schedules might mention activities such as "tennis practice," "doctor's appointment," and "movie night with partner." Looking at these schedules, you might want to apply codes such as "fun activities" and "important tasks" to these items to get a sense of how often each category of activity occurs in people's everyday routines.


Coding your data can be a time-consuming process, but required when applying inductive reasoning to your research data. Traditionally, researchers code one document, or source of data, at a time.

In ATLAS.ti, tools like the Text Search function can quicken the coding process by allowing researchers to search for a specific word or phrase in their project and code segments containing their desired search term. If a particular code can be represented by a certain word or phrase, the Text Search tool can allow you to organize the relevant data in one place for quick and easy coding. You can use the Word Cloud to inductively identify specific words or phrases and then code for these using the Text Search tool.

inductive analysis of data

The Text Search function also works with deductive reasoning, particularly when existing theories can be associated with particular words or phrases you can look for in your project. Whatever the approach, ATLAS.ti can help you save time in coding your research.

Further data analysis

Once your data has been coded, you can look at the Code Manager to examine which codes have been used the most. This will aid the inductive reasoning process by identifying what occurs the most often in your data.

Not only can you apply inductive reasoning through the occurrence of codes, but also the co-occurrence of codes as well. Keep in mind that quotations can contain multiple codes and that quotations with different codes can overlap.

When text is associated with more than one code, those codes co-occur with each other. Researchers can use that co-occurrence to infer relationships between different phenomena.

ATLAS.ti has a tool called Code Co-Occurrence Analysis , where you can examine codes generated through inductive reasoning and identify potential relationships between those codes. The Code Co-Occurrence table lists the frequencies for different pairs of codes that you specify in ATLAS.ti.

Drawing conclusions

Codes based on inductive reasoning are often brought together into a theory or framework. You can look at both frequently occurring codes as well as codes that appear even only once or twice to build premises for your theory. What is most important is that the different parts of your theory fit together in a coherent manner and explain the phenomenon under study. Building conclusions relies on first drawing tentative conclusions and then verifying these conclusions in the data. You might adjust your conclusions as you find different examples or disconfirming evidence. This iterative process contributes to building a meaningful theory or framework.

Frequencies of code co-occurrence represent potential relationships between codes that are potentially useful to theoretical development. The frequency counts for codes and code co-occurrences can all be exported into Microsoft Excel using ATLAS.ti's export functions. By exporting these counts into a spreadsheet, researchers can then run further statistical analysis on their project. More complex statistical analyses can also be conducted by exporting the entire ATLAS.ti project and importing it into a statistical analysis software, such as SPSS or R.

A more holistic research inquiry can start with inductive research methods but should look at different approaches to research in order to fully understand a particular concept or phenomenon. You may want to collect data for deductive research to apply your theories developed through inductive reasoning to new information, or you may look at abductive reasoning to look at your object of inquiry in an entirely new way. Synthesizing your research with a quantitative approach may also be useful if you are looking to identify any statistical generalization in your research inquiry. Whatever your research, however, you can benefit from addressing your research questions from multiple angles.

Abductive reasoning

While the use of deductive versus inductive approaches in research is often discussed, abductive reasoning is the third type of reasoning that also warrants some attention.

Abductive reasoning can be seen as sitting between inductive and deductive forms of reasoning. Abduction involves developing an argument based on the information available in your data and then verifying or further elaborating on these inductive findings by referring to existing theories. Iterating between data and literature thus informs abductive analysis.

Employing quantitative research

Theories built on inductive reasoning can be followed up by quantitative research to confirm the research through statistical generalizations. Generally, any research that employs deductive reasoning can be used to support inductive inferences. Still, quantitative research at scale is useful in confirming the applicability of theory across large populations or multiple contexts.

Regardless of the reasoning or methodology employed, all good research has the capability of generating, strengthening, and extending theory when it incorporates sound, transparent analysis. ATLAS.ti can facilitate the analytical process of research by making the coding process faster and more intuitive so that researchers can spend more time critically reflecting on their analysis and developing theory.

inductive analysis of data

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inductive analysis of data

Inductive content analysis: A guide for beginning qualitative researchers

  • Danya F Vears Murdoch Children's Research Institute Melbourne Law School, University of Melbourne
  • Lynn Gillam Children's Bioethics Centre, The Royal Children's Hospital Melbourne School of Population and Global Health, University of Melbourne

Inductive content analysis (ICA), or qualitative content analysis, is a method of qualitative data analysis well-suited to use in health-related research, particularly in relatively small-scale, non-complex research done by health professionals undertaking research-focused degree courses. For those new to qualitative research, the methodological literature on ICA can be difficult to navigate, as it employs a wide variety of terminology and gives a number of different descriptions of when and how to carry it out.

In this article, we describe in plain language what ICA is, highlight how it differs from deductive content analysis and thematic analysis, and discuss the key aspects to consider when making decisions about employing ICA in qualitative research. Using a study investigating practices and views around genetic testing in children as an example, we provide a clear step-by-step account of analysing text using ICA. 

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Qualitative Research Journal

ISSN : 1443-9883

Article publication date: 31 October 2018

Issue publication date: 15 November 2018

The purpose of this paper is to explain the rationale for choosing the qualitative approach to research human resources practices, namely, recruitment and selection, training and development, performance management, rewards management, employee communication and participation, diversity management and work and life balance using deductive and inductive approaches to analyse data. The paper adopts an emic perspective that favours the study of transfer of human resource management practices from the point of view of employees and host country managers in subsidiaries of western multinational enterprises in Ghana.


Despite the numerous examples of qualitative methods of data generation, little is known particularly to the novice researcher about how to analyse qualitative data. This paper develops a model to explain in a systematic manner how to methodically analyse qualitative data using both deductive and inductive approaches.

The deductive and inductive approaches provide a comprehensive approach in analysing qualitative data. The process involves immersing oneself in the data reading and digesting in order to make sense of the whole set of data and to understand what is going on.


This paper fills a serious gap in qualitative data analysis which is deemed complex and challenging with limited attention in the methodological literature particularly in a developing country context, Ghana.

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Azungah, T. (2018), "Qualitative research: deductive and inductive approaches to data analysis", Qualitative Research Journal , Vol. 18 No. 4, pp. 383-400.

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Dr Deborah Gabriel

Dr Deborah Gabriel

Inductive and deductive approaches to research

inductive analysis of data

The main difference between inductive and deductive approaches to research is that whilst a deductive approach is aimed and testing theory, an inductive approach is concerned with the generation of new theory emerging from the data.

A deductive approach usually begins with a hypothesis, whilst an inductive approach will usually use research questions to narrow the scope of the study.

For deductive approaches the emphasis is generally on causality, whilst for inductive approaches the aim is usually focused on exploring new phenomena or looking at previously researched phenomena from a different perspective.

Inductive approaches are generally associated with qualitative research, whilst deductive approaches are more commonly associated with quantitative research. However, there are no set rules and some qualitative studies may have a deductive orientation.

One specific inductive approach that is frequently referred to in research literature is grounded theory, pioneered by Glaser and Strauss.

This approach necessitates the researcher beginning with a completely open mind without any preconceived ideas of what will be found. The aim is to generate a new theory based on the data.

Once the data analysis has been completed the researcher must examine existing theories in order to position their new theory within the discipline.

Grounded theory is not an approach to be used lightly. It requires extensive and repeated sifting through the data and analysing and re-analysing multiple times in order to identify new theory. It is an approach best suited to research projects where there the phenomena to be investigated has not been previously explored.

The most important point to bear in mind when considering whether to use an inductive or deductive approach is firstly the purpose of your research; and secondly the methods that are best suited to either test a hypothesis, explore a new or emerging area within the discipline, or to answer specific research questions.

Citing This Article

Gabriel, D. (2013). Inductive and deductive approaches to research.  Accessed on ‘date’   from

Gabriel, D., 2013. Inductive and deductive approaches to research.  Accessed on ‘date’  from

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200 thoughts on “ Inductive and deductive approaches to research ”

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Hi, yes the explanation was helpful because it was simple to read and pretty much, straight to the point. It has given me a brief understanding for what I needs. Thanks — Chantal

It was very supportive for me!! thank you!!

thank you so much for the information. it was simple to read and also brief concise and straight to the point. thank you.

Deborah, thanks for this elaboration. but I am asking is it possible to conduct a deductive inclined research and also generate a theory, or add to the theory. I have been asked by my supervisor whether I am just testing hypothesi or my aim is to contribute/generate a theory. my studies is more of a quantitative nature. Thanks

Deductive research is more aimed towards testing a hypothesis and therefore is an approach more suited to working with quantitative data. The process normally involves reproducing a previous study and seeing if the same results are produced. This does not lend itself to generating new theories since that is not the object of the research. Between inductive and deductive approaches there is also a third approach which I will write a post on shortly – abdductive.

Dear Deborah, it has been very long time since you posted this article. However, I can testify that it is very helpful for a novis reseracher like me. 

Ms. Deborah, thank you for given info, but i was in confusion reg, differences between deductive, inductive, abductive and (new one) Hypothetico-deductive approaches. Can it be possible to email the differences, its applications, tools used and scientific nature, to build a theory using quantitative survey method. My email Id is: [email protected] Iam writing my PhD thesis based on this . it is part of my 3 chapter. Thanking you awaiting for your mail soon.

Hi Madhu. As a PhD student you need to take the time to read the appropriate literature on research approaches and attend workshops/conferences to better understand methodology. You cannot take short cuts by by asking someone (me) to simply provide you with ready answers to your queries – especially when I do not have the time to do so!

Great saying…every student at every level, should be critical analysts and critical thinkers.

Thank u.. but there is a little mistake which is deductive approach deals with qualitative and inductive appoech deals with quantitative

No – you have it the wrong way round – I suggest you read the article again and also engage in wider reading on research methods to gain a deeper understanding.

A clear cut concept with example.

Deborah I appreciate so much in this article…I got what I am looking for… thanks for your contribution.

Thanks so much

It is simple, easy to differentiate and understandable.

Thank u for the information, it really helps me.

Exactly, your work is simple and clear, that there are two research approaches, Inductive and deductive.Qualitative and Quantative approaches You gave clear differences in a balance, simple to understand, I suppose you are a teacher by profession.   This is how we share knowledge,and you become more knowledgable

Thank you Lambawi, I am glad that these posts are proving useful. I will endeavour to add some more in the coming weeks!

This has been helpful. thanks for posting

Thank you very much for sharing knowledge with me. It really much helpful while preparing my college exam. Thank –:)

Thank you ever so much for making it simple and easily understandable. Would love to see more posts.

Best wishes

The explanation is simple and easy to understand it has helped to a lot thank you

very helpful and explained simply. thanks

Explanation is simple…. it was a great help for my exam preparation.

Excellent presentation please!

Very helpful information and a clear, simple explanation. Thank you

Thanks; this has been helpful in preparation for my forthcoming exams

This is fantastic, I have greatly beneffitted from this straight forward illustration

Thanks…i will benifited to read this

Thanks for your help. Keep it like that so that will be our guide towards our destinations.

Thank-you for your academic insights.

Thank you for your clarification. Well understood.

Hi, I had a question would you call process tracing technique an inductive or deductive approach? or maybe both? Hope you can help me with the same.

Hi Achin. Process tracing is a qualitative analytical tool and therefore inductive rather than deductive, since its purpose is to identify new phenomena. You might find this journal article useful:

Preparing for my Research Methods exams and I'm grateful for your explanations. This is a full lecture made simple. Thank you very much.

I am very thankful for this information, madam you are just good. If you are believer, allow me to say, May God bless you with more knowledge and good health.

Hi Rasol, glad that the post was useful and  – yes – I am a believer so thank you for the blessing!

Dear Deborah 

I am currently doing Btech in forensic with Unisa would you be so kind and help with this question below and may I use your services while Iam doing this degree Please 

Hi I have question that goes like these "If the reseacher wanted to conduct reseach in a specific context to see whether it supports an establisblished theory" the reseacher would be conducting 

1. case study 

2. deductive research 

3. exploratory resarch 

4. inductive methods 

please help me to choose the right one 

Yours Faithfully 

Baba Temba 

Deductive, which is not exploratory but designed to test a hypothesis. So this is unlike to be case study research but a quantitative study.

Hi Deborah, I have been struggling with my research methods proposal, in finding the right methodology for my study.  This is the only explanation out of all the books that I have read which really enables me to truly understand the meaning of Grounded Theory for which you describe as an inductive.  I just would like to say thank you for your explanation as this has helped me in a way, which I thought I would never get.  Thank you Destini 

You are most welcome!

Very useful piece of information. Thank you!  

Very impressing work, may god bless you with more mighty knowledge.

In fact this has been very usefull information for me in my research,. It's very clear and easy to understand looking at the choice of language ,etc God bless you!!

Hi Ms Deborah Gabriel, I am from the backward area of Pakistan, which is known as “North Waziristan”. Unfortunately famous for terrorism, as from that background, you can understand the weakness of my educational background. I am struggling for MBA degree, and I was searching for Deductive and Inductive approaches, and then I found your best explained article here, already praised by many people. I will just add this “Thank you, May GOD Bless You” I will be highly honoured if you would like to contact me on my email. My email is [email protected] Thanking you for your time and efforts.

Is it possible to use deductive approach in research concerning what has happened in an industry?

If you are seeking to test a hypothesis then yes. 

Thank you very much this information has been extremely helpful. I can now progress with my dissertation. 

Thanks for that good work Deborah. It has taken me quite a short time to read and understand. Kindly please help me understand what am required to write in this case where my teacher gave me this question: "Explain the process of deduction and induction research approaches".

Please refer to the recommended reading:

Many thanks to you, I really appreciate u on ur information provided basically on theories and approaches to understanding research.

Thank you very much.

Good work Deborah.

Thank you so much!! The distinction between the two approaches is clear and concise. Most other websites tend to go into long discussions without really getting to the point. This was very helpful. 

Thank you , useful explanation

It is a very fruitful post. I would like to ask if the objective of my research is to develop an extended process from the existing processes. And I am going to use qualitative and quantitative research methods, because my research phenomenon requires to study the individual meanings and perceptions and then uses the findings from the qualitative study and also the theoretical study as inputs for the quantitative study. Finally, I will use the findings of the theoretical study and the quantitative study in developing the extended process. So, which approach to follow in this case?

Dear Tamer, Your question is too hypothetical for me to offer a response. But in any event, you are the only one who can decide whether an inductive or deductive approach is appropriate for your research project. This is where methodology comes in – which is about determining what research methods will be most effective in answering your research questions and which are in sync with your approach (e.g. critical, feminist etc). 

my dissertation is in the same situation. and I also feel struggle to choose my research approach. I guess its a combination of inductive and deductive. using the deductive approach to test what was found in the literature, and use an inductive approach to examine the themes that emerged from qualitative data.

Thanks much! 

What do you think about the approach with quantitative analyses that start with data to generate theories? Typically data mining techniques fit into my example. 

This is a question of methodology – research methods must be selected based on the discipline, research questions and approach to the study. For example, If you are seeking to ascertain how many people read the news on their smartphones then a quantitiative method is most appropriate. On the other hand. if you are seeking to delve into why  some people read the news on their smartphones, then clearly a qualitative method is required.

Awesome response, I was looking at the same thing in my postgraduate class.

What if I’m using secondary sources? Which would be more appropriate qualitative or quantitative?

The question of inductive or deductive approaches arise only in relation to ‘primary’ research – that is when you are undertaking your own study. In your own study, secondary sources would appear under a Literature Review. However, if you are doing a dissertation, say for an undergraduate degree where you are not undertaking primary research then inductive or deductive approaches are not applicable. I hope this clarifies.

Your comments are really good and easy to understand. Hope to contact you for my project. keep up the good work. thank you

The last paragraph stated ‘The most important point to bear in mind when considering whether to use an inductive or deductive approach is firstly the purpose of your research; and secondly the methods that are best suited to either test a hypothesis, explore a new or emerging area within the discipline, or to answer specific research questions. However my question is if my research is about answering specific research questions in a qualitative research. Am I to use the inductive or the deductive or the mixture of the two?

Hi Ola, if your research questions are qualitatively focused – that is seeking to find out the whys and the hows as opposed to ‘what’ and ‘how many’ then certainly an inductive approach is most appropriate. This is because inductive aims to find new theories emerging from the data whereas deductive is centred on testing a hypothesis rather than exploring research questions.

Thank u so was difficult for me to understand but with ur help the job is complete

Points of distinction top notch. Absolutely fantastic. Straight to the point. Was really helpful. Keep up the good work.

Thanks for the inforation Deborah. It was  useful

Thank you so much, this was something I was never able to grasp so well! I found this site while searching the difference between the two on Google. I am a PHD Scholar, now it seems I will be visiting this site frequently and seeking your help 🙂

Hi Deborah. Thank you for the input.It clearly exemplifies the difference. In your response to one of the questions, you have highlighted a lot of 'what'  will qualify the research as quantitative. I have developed 4 research questions, 3 are on 'what's and 1 'why'. The what is because my sample of analysis is multimodal text. Will my study still fall under qualitative? Thank you in advance, Deborah. I appreciate it very much. 

Hi Zilla, It is hard to provide a definitive answer without knowing what your research questions are (although time does not permit me to provide individual responses). So I will reiterate that the question of whether to adopt an inductive or deductive approach to a research project is relevant for ‘primary’ research – that is, research that you undertake yourself. Factors that influence your decision should rest on whether you are seeking to explore the ‘whys’ and ‘hows’ of human experience, generating new levels or understanding or simply wish to test a hypothesis or use a large sample in order to generalise results to the wider population. You say that your sample is multimodal text – that is simply text plus media such as videos, pictures etc. My question to you is whether this multimodal text has been generated from primary research – i.e interviews you conducted, photos you took and/or videos that you filmed of research subjects? If that is the case then I would presume that this would be a qualitative research project that would lend itself to an inductive approach,since I cannot imagine that you would be able to work with a very large sample of multimodal text. If the multimodal text is not generated from your own primary resesarch then this is secondary research that might be included in your literature review but would fall outside the scope of your analysis.

Dear Deborah I just want to ask you to help me with generation of theory. Steps that need to be followed

Mongwai Michael

Thanks a lot for showing me the best way to understand the basic difference between two approaches of research.

Dear Aliyu, time does not permit me to provide responses on your individual projects. Therefore, my aim is to equip you with the understanding of different approaches so that you have both the confidence and competence to make appropriate decisions on the most suitable methodological approaches to your research.

Beautiful stuff you are giving us Deborah.

Deborah, thank you so much for your explanation, I'm clear now.

I am gathering quantitative data to develop a model to represent the behavior of a material using an existing model. I subsequently used this model to simulate the material behavior with a computer program. this is a reversal approach to previour reaeasch in these area. usually the computer simulation is used to obtain quantitative data without experiment. Could you please kindly let me know what is my reasearch method Thanks

Please see my response to Aliyu on 8 November.

Dr, your explanation about inductive research and deductive, is meaningful to postgraduate students. What is your suggestion on my research topic: use of handset by primary six pupils for games, rather for home works and readings, what is the research approach that will be suitable?

Very brief and well explained. Thank you Deborah.

Thank you Dr. Gabriel, good informationl; will come back. 

It has actually helped – a similar question was asked last year in my schools,that prompted me to search for it while preparing for my exam. Today the same question appeared and I used your explanation as my response to the question. Thanks.

Hi Deborah, your explanations are comprehensible. I understand this topic thanks to you. May I ask you question? What are  the similarities between inductive and deductive reasoning?

That’s like asking what the similarities are between quantitative and qualitative research! Focus on what your research objectives are and then choose the approach that will be most efefctive in meeting these objectives. 

Thanks so much Have got what I really want here

Enlightening facts. Thank you.

Thanks Deborah for the explanation but, i want to ask if descriptive is inductive or deductive approach? God bless you

it is really good explanation

Can I ask one question? I am going to research how technology is changing the hotel industry particularly at the hotel front desk so is that inductive or deductive approach? I believe deductive approach because the aim of my research is to investigate current used technology at hotel front desk. So what do you think please let me know Thank you very much indeed.

Please refer to my post on conceptual frameworks to take you through the key steps in developing a research project – you will find your answer there: 

The information provide is quite helpful, thanks after all….

I was confused about these approaches but your information has helped me a lot – reasonable and authentic.

I've got the answers,thx.

It is very clear and concise.

Thank you, it was right on point.

Thank you, I used this solution for my assignment.

Thank you so much Deborah. I am currently doing my dissertation and most of my lecturers have recommeded us to use Research Methods for Business Students by Mark Saunders, Philip Lewis and Adrian Thornhill. I have found the book very hard to understand especially when I'm wrtiting up the methodology section as I have to talk about deductive and inductive approaches.  You have simplified it and explained it well. Also you have made it so so easy to understand. Everyone should be reading this. Thank you so so, so much.

You’re very welcome. Good luck with your dissertation!

Thank you D now I am aware of these two!

I found it so easy to understand the difference between deductive theory and inductive- it's so helpful. Many Thanks. 

Deborah, your work is precise,well organized and relevant.Thank you very much.

Thanks for the explanation, it has cleared my doubts. 

Thanks Dr. Deb, I am satisfied – it was really useful.

Hi Doc, thank you for making things simpler for me. I will always be incontact with your website. Stay forever blessed. 

Thank you for the information. It really helps me.

Hi Deborah, i just went through the abductive approach which is combination of inductive and deductive Approach. I found it a little confusing when I tried to know by my own from e sources. But after going through the conversation in this page helped me a lot. Thank u very much. If u can share your email I can share my report made for my pre PhD comprehensive viva. My profession is teaching and my area of research is International HRM. Title is Knowledge and Learning Model among effective repatriation . If anybody is doing reserach in the same area, plse feel free to reach me at [email protected]. Thank u all again

Thx for the information.

Hi Deborah Thank you very much for the article. it is informative. My question is what approach am i supposed to take if i am doing a research that is both qualitative and quantitative. I am doing research on the feasibility of establishing renewable energy systems in a developing country. I am using a simulation software to generate a model to analyse the technical and economic data (Quantitative) but i have to use interviews to capture social and polical views from industry experts (Qualitative). So which approach is best in such a scenario? Thank you

In a mixed methods study, the quanitiative dimension of the study usually functions to capture preliminary data, with the qualitative dimension being the primary method that answers the research questions. In any case, in a mixed methods study you must peform both quantitative and qualitative data analysis – separately. In reference to your specifc study you need to refer back to your reearch questions and the aims and objectives of your study. Is your primary objective to develop a model for a renewable energy system or is it to determine whether industry experts see the viability of the model? If it is the latter then the approach should be inductive. I would advise you to consult your supervisor or someone in your discipline, as I am not an engineer.

Very informative.

Your explaination of inductive & deductive approaches to research is clear to understand. 

Your explanation of concepts is succint and easily conceivable. Helpful.

Thank you so much Deborah. I am currently writing my research on resource curse theory and I will like to have your private mail for private discussions. Thank you

No problem – you can use the contact form and your message will go directly to my email address.

Thanks for differentiating the two in easy and pragmetic manner.

Thank you Deborah, that was a simple, clear explanation helpful for sure.

I like the way you simplified everything,was really helpful for my assignment. Please how do I reference this work? Thank you

Reference it as an online source:

Gabriel,D.(2013). Inductive and deductive approaches to research. Avaolable from:  

Thank you Deborah, this is very helpful to me and others.

Great insight, simple and clear; I now get the difference thanks for sharing.

Thanks for the very good explanation and comparison.

A very simple and straightforward guidance to students. 

Hi Debrah , It is really interesting to get valuable points from your statments about deductive reasoning. However it seems short . It will be helpful for us if you write more. Thanks

Dear Almaz, thank you for your feedback. The post was only intended to provide a brief overview of the subject – to better understand inductive and deductive approaches to research I strongly recommend immersing yourself in the available literature. 

Perhaps, you can suggest 1 or 2 widely cited scholars (to read) that argue that deductive approaches can also be used in a qualitative methodology which is interpretive/subjective in nature.

Thank you once again!

You are definitely on point.

Thank you so much.

Thank you so much Deborah.

Such inspiring work.

Your literature is helping us a lot here at the Ivory Tower Makerere University Kampala Uganda.

I am glad to hear that. What are you studying?

Hi Deborah. I am Iftikhar from United Arab Emirates (UAE).  I am conducting a research on learning preferences of Generation Z youth, and one of my research questions is "What are the learning preferences of UAE Gen Z youth and how matching of L&D program delivery with these learning preferences affect Gen Z interest in organizational L&D programs?"  Now as for existing literature, a lot has already been written on this but in the West; there is practically no formal research literature available on this topic in UAE.  Therefore, I am taking the Western literature outcomes and applying these in UAE context to see the results. My questions are: a. Will this research be treated as "Deductive' or "Inductive"? b.  Should I choose 'Quantitative" or "Qualitative' approach? Wishing you all the best.  

Thanks Deborah. but I'm confused. You said deductive approach is used in quantitative research and it test a theory and inductive approach is used in qualitative research to generate a theory. So what is grounded theory?

Thanks a lot for such a good explanation, Deborah!

Thank you very much. It was simple to understand.

Lovely….this was very helpful…simple and straightforward. 

Thanks. It is so useful. Best Regards.

This has been troubling me for a while. It is often said that the interpretive paradigm typically goes with inductive approaches and methods involving observation, interviews and research into archives. But then if concepts are to emerge from the data without theoretical preconceptions, how come it is often said that the research design, choice of case studies, and initial coding in thematic analysis can be theory driven? Wouldn’t that make the approach deductive (i.e. about testing theory). Or, how does theory coming before the research design fit with an inductive approach? In my experience so far authors seem to evade this point.


Thank You so very much Deborah. I really got to uncover what puzzled me on deductive versus inductive approaches.

Thanks Dr.Gabriel. It was very simple and useful. Now I understood the differecne b/w deductive and inductive method.  

Thanx for sharing with us. It is very useful for my dissertation.  Your topic clarifies the difference between inductive and deductive research.

Hi Dr.Gabriel, I am doing a research to apply a theory into service industry which is more commonly practiced in manufacturing industry ( known as Lean approach), my aim is to apply this approach into banking operation, the objective is to find the elements/processes in the bank operation that actually increase the cost or decrease the service quality. If I want to conduct a research to find those elements in a bank operation. should I use Inductive approach? what is your advise?  Thanks 

Hi Deborah, Thank you for a great article! It made it very clear the differenece between deductive and inductive.  I'd like to ask you the following: – Is possible to have inductive study with hypotheses and use semi-structured interviews to answer these hypotheses and research questions?   thank you very much for your reply!   Alina

Hi Alina, I’m glad my post has been useful for you. In answer to your question, I think maybe you are confusing research questions with hypotheses. Research questions guide the overall study and ensure that when designing interview questions – they are structured in the most effective way to elicit responses that address the research questions. Hypotheses are linked to deductive studies where researchers aim to test presumptions/predictions about phenomenon – this is not the same as research questions.

Hi Deborah Thanks for an intersting piece of work presented. Am kindly inquiring how i can get along with literature review and conceptual framemework on the topic 'IDPs and Solid Waste Management' and objectives; exploring everyday practice around solid waste management; finding out how social networks move and merge into new spaces for waste management and establish connections between waste management and social lfestyle. Thanks Hakimu

Hi Deborah thank you for a great article . I have a question for you I am doing my research work and I have some issue about theory construction. Basically I am a beginner in social research – I have no idea about constructing new theory. Please let me know about theory construction and what is a procedure  – how can I construct theory and also about steps of this method? I hope you will understand my words. Thank you.

Dear Amna, Welcome to the world of research – we all have to start somewhere! If you're new to social research I would recommend you join the Social Research Association (SRA) who provide training and a wealth of resources for researchers. With regards to theory – unless you are researching new phenomena that has never been researched before or are developing a completely new approach (unlikely) you will not be creating 'new' theory with your research project. You will be using existing theory in your approach and embed theoretical perspectives into your methodology. You will also likely use relevant theories when analysing your data. However, before you think about theory you need to develop your methodology – see my other post: methods and methodology .

Thanks so much. This post was very helpful and easy to understand!

Hi Deborah, Thank you for the precise and helpful information .. I need your help as I feel a little bit confused. i am doing a case study of airline corporate image. it is the newest crisis scenario in my country related to our regional carrier. I think, i among the pioneers doing the case study research for this airline company. I used the conceptual framework from other previous conducted study. It was conducted in quantitative manner. If i used the conceptual framework as my guidance for my literature review and interview question construction, is that okay if i do not use inductive for the case study because i do not build a new theory. If i just compare and argue with the previous finding and the model used, is it consider as deductive approach in case study? Based on my reading, i found some researchers used deductive approach in their case study. they tested the hypotheses..but i just compare my finding with the model used from the previous research. For your information, i did documentation, direct observation and interview (trigulation) with ex-passengers and aviation expert. What do you think? .Please help me..i am stuck. Thank you

Thank you Doctor, it’s straightforward valuable piece of knowledge. It may require a little bit of referencing. Furthermore, adding citation line below will be useful for academic use.

Thank you very much!

Thank you for a crisp and nice post. It helped me.

I am very beginner in research, and its really very helpful.

Thanks writer,

Thank you very much…

wow wow wow great work Deborah. I now have a clear insight of the differences,,,, kudos!

Thank you …it’s helpful for me.

Good job.It helped me to find the question’s answer in my mid-term. Thenk you.

My study is ethnographic research specifically it studies about culture, tradition and lifestyle of an ethnic groups. I think my research is inductive, is it right?

Thank you, I feel same as most the above commentators. Very well written – written in a way that I (who for the first time heard of these two types of researcher methods,) felt like I got a gist of what they are and how they are different.

Thanks Deborah G! Your articles have helped me a lot.

your article is simple to understand and please keep it up thanks.

Hi, It is really helpful me to get sorted these concepts in research field in simple manner.Thanks for that and really appreciate it.

Very clear explanation about inductive and deductive appraoches in research. I like it.

I appreciate your clear and precise explanations, thank you.

Thanks for this, it’s clear, concise and easy to understand – very helpful.

That was just perfect. I have been reading about these methods from my course book offered by university for 3 days but I couldn’t understand their differences. Now it is completely clear. Thanks a lot.

Is that possible to have both in our research? I mean, what if we choose an inductive approach and then when we go forward make some assumptions to answer research questions?

If you are undertaking inductive research then you don’t make any assumptions as you are looking for micro theories to emerge from your analysis of the data. You cannot start with inductive and then switch to deductive – it must be one or the other. You don’t make assumptions to answer research questions – you analyse the findings to do that.

It is precise and clear. Thanks

Great work and explanation and also the researcher herself is very energetic and motivated to help others… world is because of people like you. thanks

Dear madam, I’m a undergraduate studying engineering and I want to know famous researches done solely based on inductive and deductive separately….if you can give me some examples it will be very helpful to me….

You can look this up yourself, through your library and learning resources at your institution…

Dear Deborah, Thank you for the precise explanation of inductive and deductive approaches.

When analysing data in a qualitative study, could you use both inductive and deductive methods as a triangulation technique?

Hi Irene, a mixed methods study might involve both a quantitative method – e.g. survey and qualitative – e.g. interviews. But the overall approach would still be inductive as the quantitative element normally shapes the qualitative and the overall aim would still be to gain in-depth understandings rather than generalise findings. Mixed methods does as you say, create academic rigour through triangulation.

Dear Deborah Thank you in advance for using your precious time to reply to my question. God bless you. l am doing my master degree dissertation on Green Supply Chain Management practices in the United Kingdom automotive industry. The research philosophy that I adopt is this: interpretivist epistemological and constructivism ontological. The methodology is as follows: Interpretivism – inductive – mono-method qualitative -survey. My question is this: Can online survey questionnaire be used with the inductive approach? Most books that l am reading are linking online survey with quantitative data.

Quite educative. Thank you Dr Deborah, it’s so useful to my study.

Thank you Dr Gabriel. It was really useful abstract. Kindly help me to enlighten with more details on grounded theory, dependent and independent variables.

Thank you Dr Gabriel for sharing your knowledge with all of us. Highly appreciated. I have just started my Ph.D. program, and I’m still struggled to deciding which paradigm I have to use! I have one question: My research idea talks about the readiness of an organization toward IoT (factors that affect the organization readiness towards IoT). note*: The IoT technology is still not implemented in the organization context that I want to study that is why I’m going to study the readiness. so what is your suggestion for me regarding which paradigm I have to use?

Hi Mutasem, Congratulations on embarking on your PhD program! Your research paradigm should reflect your positionality, your values and in essence, how you view the world. You need to think critically and reflectively about this. For example, you say you plan to study the readiness of an organization to implement IT. One approach to this study could be examining what factors might shape that readiness – i.e power structures that confer equality/inequality, and there is also the question of how the adoption of IT could help to create more inclusivity and diversity (which contribute to greater productivity & profitability). This of course is a different proposition from merely focusing on technical issues as opposed to social/political ones that also shape technology use.


Dear Dr Gabriel

I am currently busy with my Masters in Interior design. My research aim is to determine (possibly explore as Its not currently making sense) the discrepancy that exists between the designer and a specific user group of a Healthcare environment. I have used provisional coding as a first cycle method (which identified a set of themes by which to analyze a Healthcare environment). These themes (conceptual framework) informed my interviews etc. From there the findings were analysed to see to what extent the designer aims correspond with the way in which the user group experiences the space (through the various themes). I initially thought that it was a deductive study but as it is a qualitative study I was abit worried. From what I understood from previous comments, the inductive/deductive is only applicable in the primary research, would that then mean that my study takes the form of a inductive approach? (although my questions are ‘what’ and ‘how’).

Kind regards Anienke

Hi Deborah, your posts are quite simple and useful. Great! Thanks for posts!

Thanks for posting this, I would say that this article is one of the most useful explanation I have found so far. But I also have a question, hopefully you will be able to help me out. If my research question is about understanding “How important are loyalty programmes for customers in welness field. I use quantitative method (Online survey) to collect data from respondents, what is my approach for research inductive and deductive?

Dr Deborah Gabriel, Honestly, I have gone through your explanation on inductive and deductive approaches to research work and I’m very pleased with the write up. I want to sincerely thank you for your contribution to the existing body of knowledge. Regards, Clemduze.

Thank you Dr Gabriel for explaining the differences between inductive and deductive approaches in research. Your explanation helped me understand these two concepts as I am working on the early portions of my dissertation in General Psychology.

Thank you so much. This has been very useful. I now know I can use both Inductive and deductive if i am carrying out a mixed method research. Both can be used depending on my research questions.

It was helpful.

How can I be at this time replying to this worthy and simple explanation? It’s well placed and and explained. Thank you D.

Thanks for your article. How do I reference you in my work?

APA Gabriel, D. (2013). Inductive and deductive approaches to research. Accessed on ‘date’ from

Harvard Gabriel, D., 2013. Inductive and deductive approaches to research. Accessed on ‘date’ from

Can social research be purely deductive?

‘Social’ research – i.e research within the social sciences, can be qualitative or quantitative, and therefore can be inductive or deductive. It depends on what the research objectives are as to which approach is taken – and as my article states, these questions are explored through research methodology.

Very important points are discussed in your article in simplified terms.

Thanks very much!

Appreciated the discussion – it is well simplified and easy to understand.

Comments are closed.


Inductive Approach (Inductive Reasoning)

Inductive approach, also known in inductive reasoning, starts with the observations and theories are proposed towards the end of the research process as a result of observations [1] .  Inductive research “involves the search for pattern from observation and the development of explanations – theories – for those patterns through series of hypotheses” [2] . No theories or hypotheses would apply in inductive studies at the beginning of the research and the researcher is free in terms of altering the direction for the study after the research process had commenced.

It is important to stress that inductive approach does not imply disregarding theories when formulating research questions and objectives. This approach aims to generate meanings from the data set collected in order to identify patterns and relationships to build a theory; however, inductive approach does not prevent the researcher from using existing theory to formulate the research question to be explored. [3] Inductive reasoning is based on learning from experience. Patterns, resemblances and regularities in experience (premises) are observed in order to reach conclusions (or to generate theory).

Application of Inductive Approach (Inductive Reasoning) in Business Research

Inductive reasoning begins with detailed observations of the world, which moves towards more abstract generalisations and ideas [4] . When following an inductive approach, beginning with a topic, a researcher tends to develop empirical generalisations and identify preliminary relationships as he progresses through his research. No hypotheses can be found at the initial stages of the research and the researcher is not sure about the type and nature of the research findings until the study is completed.

As it is illustrated in figure below, “inductive reasoning is often referred to as a “bottom-up” approach to knowing, in which the researcher uses observations to build an abstraction or to describe a picture of the phenomenon that is being studied” [5]

Inductive approach (inductive reasoning)

Here is an example:

My nephew borrowed $100 last June but he did not pay back until September as he had promised (PREMISE). Then he assured me that he will pay back until Christmas but he didn’t (PREMISE). He also failed in to keep his promise to pay back in March (PREMISE). I reckon I have to face the facts. My nephew is never going to pay me back (CONCLUSION).

Generally, the application of inductive approach is associated with qualitative methods of data collection and data analysis, whereas deductive approach is perceived to be related to quantitative methods . The following table illustrates such a classification from a broad perspective:

However, the statement above is not absolute, and in some instances inductive approach can be adopted to conduct a quantitative research as well. The following table illustrates patterns of data analysis according to type of research and research approach .

When writing a dissertation in business studies it is compulsory to specify the approach of are adopting. It is good to include a table comparing inductive and deductive approaches similar to one below [6] and discuss the impacts of your choice of inductive approach on selection of primary data collection methods and research process.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  contains discussions of theory and application of research approaches. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research design ,  methods of data collection ,  data analysis  and  sampling  are explained in this e-book in simple words.

John Dudovskiy

Inductive approach (inductive reasoning)

[1] Goddard, W. & Melville, S. (2004) “Research Methodology: An Introduction” 2nd edition, Blackwell Publishing

[2] Bernard, H.R. (2011) “Research Methods in Anthropology” 5 th edition, AltaMira Press, p.7

[3] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th  edition, Pearson Education Limited

[4] Neuman, W.L. (2003) “Social Research Methods: Qualitative and Quantitative Approaches” Allyn and Bacon

[5] Lodico, M.G., Spaulding, D.T &Voegtle, K.H. (2010) “Methods in Educational Research: From Theory to Practice” John Wiley & Sons, p.10

[6] Source: Alexandiris, K.T. (2006) “Exploring Complex Dynamics in Multi Agent-Based Intelligent Systems” Pro Quest

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  • Knowledge Base


  • How to Do Thematic Analysis | Step-by-Step Guide & Examples

How to Do Thematic Analysis | Step-by-Step Guide & Examples

Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.

Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

  • How do patients perceive doctors in a hospital setting?
  • What are young women’s experiences on dating sites?
  • What are non-experts’ ideas and opinions about climate change?
  • How is gender constructed in high school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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inductive analysis of data

Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

  • An inductive approach involves allowing the data to determine your themes.
  • A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?

There’s also the distinction between a semantic and a latent approach:

  • A semantic approach involves analyzing the explicit content of the data.
  • A latent approach involves reading into the subtext and assumptions underlying the data.

Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?

After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.

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Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.

We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

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

Research bias

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

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Identifying the main themes in data from user studies — such as: interviews, focus groups, diary studies, and field studies — is often done through thematic analysis.

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A qualitative study of social accountability translation: from mission to living it

  • Jennifer Cleland   ORCID: 1 ,
  • Anand Zachariah 2 ,
  • Sarah David 2 ,
  • Anna Pulimood 2 &
  • Amudha Poobalan   ORCID: 3  

BMC Medical Education volume  24 , Article number:  145 ( 2024 ) Cite this article

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Medical schools are increasingly adopting socially accountable mission and curricula, the realisation of which are dependent on engaging individuals to embody the mission’s principles in their everyday activities as doctors. However, little is known about how graduates perceive the efforts taken by their medical school to sensitise them to social accountability values, and how they translate this into their working lives. Our aim was to explore and understand graduate perceptions of how their medical school influenced them to embody a social accountability mission in their working lives.

This was a qualitative interview study carried out with graduates/alumni [ n  = 51] of Christian Medical College, Vellore [CMCV], India, a school with a long-established and explicit social-accountability mission. Data coding and analysis were initially inductive and thematic using Braun and Clarke’s six step framework. MacIntyre’s virtue ethics theory framed secondary analysis, allowing us to consider the relationships between individual and contextual factors.

Our participants perceived that CMCV invested heavily in selecting personal qualities aligned with the CMCV mission. They saw that these qualities were reinforced through various practices: [e.g., placements in resource limited and/or remote and rural settings]; community engagement and expectations [e.g., student self-governance]; role modelling [staff and more senior students]. Much emphasis was placed on sustaining these traditions and practices over time, creating a strong sense of identity and belonging among participants, traditions which were fostered further by the alumni network and continued engagement with CMCV post-graduation.


Ensuring social accountable medical education depends on alignment and interactions over time between context and structures, systems and human agents. Further studies are needed to extend understanding of how students from diverse contexts experience socially accountable medical education and translate their educational experience into their thinking and practice after graduation.

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The World Health Organization [WHO] defines social accountability, in the context of medical education, as the responsibility to focus education, research, and service activities on tackling the most pressing health issues in the communities, regions, and countries they have a mandate to serve [ 1 ]. Increasingly, medical training institutions globally are using this framework to design their curricula [ 2 , 3 , 4 , 5 , 6 ] and prepare future doctors to be responsive to the needs of the community, region and/or nation [ 7 , 8 ].

With this has come a proliferation of research examining different aspects of social accountability in medical education. Studies in this area focus on five broad areas of inquiry: description of this mission and curricula [e.g., 9 , 10 , 11 , 12 ]; guidelines for implementing and assessing social accountability curricula and mission [e.g., 6 , 8 , 13 , 14 , 15 ]; challenges to integrating social accountability into curricula [e.g., 16 , 17 , 18 ]; who is selected into schools with an explicit social accountable mission [e.g., 19 , 20 , 21 ] and where the graduates from these schools ultimately practice [e.g., 8 , 22 , 23 , 24 ]. The few studies of student experience have focused on student perceptions of formal [planned and timetabled] mechanisms and experiences at socially accountable medical schools [e.g., 25 , 26 ]. However, we lack understanding of how students carry the mission they encounter at medical school forward into their thinking and practice after graduation. This is problematic as without examining this space we do not have a full sense of the essential elements or what matters in terms of achieving social accountable medical education. In other words, if “the realisation of a social mission is dependent on an institution’s ability to direct or persuade individuals within it to embody the mission’s principles in their everyday activities” [ 25 , p. 172], in-depth understanding of how learners experience the social accountability mission of their medical school as students and how they embrace this going forward is critical.

Therefore, to address this gap in knowledge, our aim was to explore and understand graduate perceptions of how their medical school influenced its students to embody a social accountability mission. Our specific interest is in graduates/alumni perspectives of their time at medical school and how they carried that experience with them after graduation. Graduate narratives are built in the present but are formulated and sorted based on the application of experience [ 27 ]. They have had time to put their education and career in perspective [ 28 ] and thus provide unique insight. Indeed, graduate/alumni research has been deemed important in respect of informing curriculum development and assessing learning outcomes [e.g., 29 ], but their voices are rarely heard in health professions education research.

This was a qualitative interview study underpinned by social constructionism, acknowledging that reality is produced through interchanges between people and objects and shared activities, with knowledge and the individual embedded within history, context, culture, language and experience [ 30 ].

Our context was a medical school which has had an explicit social accountability mission for over 100 years. Christian Medical College, Vellore, Tamil Nadu, India, [CMCV] was established as a not-for-profit medical school for women in 1918 [men were admitted from 1947] with the explicit principles of preparing graduates to meet health care needs of society and with social values at its core. Much teaching and learning is delivered in the community, in rural, tribal, urban and in urban slums and CMCV has links with 150 secondary mission hospitals across India. CMCV supports the mission hospitals via the CMCV graduate service obligation: students selected from local communities go back and work in these communities for a two-year service obligation. However, there is the expectation that all CMCV graduates will choose to work in areas of need and deprivation, whether these be rural or urban.

Data collection

We had three orientation discussions with senior faculty [via Zoom], the CMCV website and a site visit [November 2019] to inform the research focus. During this orientation, JC used her outsider [“etic”: 31 ] status to ask questions about the history and traditions of medical education in India generally and CMCV’s activities and processes specifically. We used this information and the social accountability literature as the basis for developing a semi-structured interview schedule. Questions included: where alumni worked [the health care provision of their hospital/organisation], why they chose this work, and their perceptions of the relationship between their medical education experiences and their practice/career choices. We tested and refined our questions via two pilot interviews. No substantial changes were required to the schedule following piloting, so we incorporated these data into the main data set.

Sampling and recruitment

We wanted to explore how CMCV created an environment that supported students to integrate their personal beliefs and motivations with CMCV’s social accountability mandate. To gather a range of perspectives, we aimed to recruit CMCV graduates/alumni from a wide variety of backgrounds, working in different regions and countries and across diverse healthcare settings.

CMCV’s Graduate Office and Alumni Directorate records mapped out details of CMCV graduates working in India, by geographic location [including urban, rural, slums], type of health work [hospital-based work, community health work, inter-sectoral involvement] and different sectors [mission/charity/government], over 50 years [graduates from class of 1960s to 2010]. In addition, we generated a list of CMCV graduates living abroad. From this list, we purposively approached respondents who represented a range of sexes, specialties, type of work and geographical spread [ 32 ]. Demographic and career data were cross-referenced via an accompanying short questionnaire prior to the interviews.

In January 2020, the CMCV Alumni office emailed invitation letters to take part in the study, the participant information sheet [PIS] and consent form to graduates. They sent a reminder after two weeks. We followed up on indications of interest in the study by arranging a face-to-face or remote interview. Formal consent was obtained before data collection. At this point, we also reminded participants that their data would be held securely and anonymized for reporting, and they could withdraw at any point during or after the interviews without explanation. As far as possible, open questions guided the discussions, with prompts from the researcher [SD] to probe for deeper understanding of participants’ views. All interviews were undertaken by SD. The interview schedule ensured consistency, but interviews were iterative. Data were collected in English between January and December 2020.

Data management and analysis

Participant interviews were iterative and digitally audio-recorded, transcribed verbatim by a third party approved by CMCV, and anonymised during transcription. In keeping with our constructivist stance, initial data analysis was inductive, thematic and reflexive [ 33 , 34 ]. SD and AP carried out the initial analysis, using Braun and Clarke’s six step framework to generate themes while constantly reflecting on their relationship with the data. JC then examined the data and SD/AP’s interpretation thereof, while constantly considering her own positionality [see later for further discussion] [ 31 ]. AP and JC had regular meetings to reflect on the data and consider how best to represent meaning as communicated by the participants. The resultant codes were then shared with the wider team for further discussion and reflection. Ideas were documented through memos and correspondence that created an audit trail of the analytical process.

In keeping with CMCV’s ethos of collaboration and engagement with its alumni [ 35 , and see Results], we also invited some of the research participants to check a draft manuscript outlining our initial interpretations [ 36 , 37 ], asking them if they could see their experiences within the results and if they wanted to add anything for us to consider [ 38 ]. Eight interviewees came back to us with feedback and comments, and we analysed these as part of the final data set.

During the inductive analysis we were struck by our participants’ references to enacting the social accountability mission of CMCV, not just in respect of where they went onto work, but how they acted within the world. Given this, we then used MacIntyre’s virtue ethics theory as a framework within which our data could be analysed. Drawing from Aristotle’s work on ethics, the contemporary philosopher Alasdair MacIntyre [ 39 , 40 ] developed a “virtues–practices–goods-institutions” framework where virtue is dispositional (qualities of character) and guides actions, but these dispositional qualities can be reinforced and sustained (or not) by the social, cultural and political context within which an individual operates. For example, a student’s intrinsic commitment to the social accountability mission espoused by the school may be reinforced (or not) through the curricular and other experiences offered, and this combination of disposition and reinforcement will inform career choices. The use of this framework thus enabled us to consider the relationships between character (disposition) and CMC (the institution, its traditions and structure), and how CMC supported the processes of learning to be virtuous in respect of social accountability. MacIntyre’s virtual ethics theory has been used extensively in sociology and organizational research [see, for example, 41 , 42 , 43 for overviews], more recently in medicine [e.g., 44 ] and medical education [ 45 ].

Rigour and reflexivity

We reflected on our backgrounds and how these may have shaped our interpretation of the data. Our identities differ in terms of ethnicity, gender, religious orientation, learning experiences and disciplinary backgrounds, research interests and personal life courses [ 46 , 47 ]. We are based in three countries, each of which represents very different contexts in terms of power and privilege, and how access and opportunity are distributed in society [ 48 , 49 ]. We also constantly considered our insider [AP [a CMCV graduate working in the UK], APul, AZ, SD] and outsider status [JC] [ 31 ]. Team discussions were thoughtful, respecting our different views and positions in relation to the data.

Patient and public involvement

Patients and the public were not involved in study planning.

Ethical approval was obtained from the Institutional Review Board [IRB] of CMC, India [IRB 12,141] and College Ethics Review Board [CERB], University of Aberdeen [CERB/2019/8/1819].

We conducted 51 interviews with a total of 54 participants: 48 individual interviews [M = 32; F = 16] and three with two graduates in each interview [medical couples who had both graduated from CMCV]. Four participants were graduates from the 1950-60 decade; 14 from 1960 to 70; 19 from 1970 to 80; 12 from 1980 to 90, two from 1990 to 2000 and three participants were graduates from 2000 onwards. While many participants had worked in diverse sectors and settings over their careers, at the time of data collection, twenty-eight interviewees were working in Mission hospitals or NGOs [non-governmental, non-profit organizations]; five in the Private/ Corporate sector and others in the Government sector. Most worked in India: 12 participants worked abroad. Half of the participants [ n  = 27] worked in rural or semi-urban areas. Interviews lasted for an average for 31 min ranging from 22 to 41 min.

We start with themes and data relating to CMCV structures: from selection, through to remote and rural placements and post-graduate service. We then present data relating to traditions and narratives around these traditions, role modelling and community expectations. Finally, we report data on “action”, on participants’ perceptions of how their CMCV training influenced their career decisions and other actions, and how they contribute to sustaining CMCV’s mission.

Quotations are included to aid confirmability and to help the reader follow the logic of the story. Participants are labelled by gender, place of work [urban/rural], type of workplace [e.g., Government hospital] and time of joining CMCV [e.g., class of 60–70].

Selecting for certain personal qualities

The orienting interviews and webpages illuminated that CMCV selected for the character qualities or values they considered necessary to work in healthcare [e.g., compassion, empathy, respect, service to the community] rather than assuming these qualities could be developed during medical education and training. To achieve this, while academic attainment and aptitude tests were used as the first stages of selection, the final stage of the selection process was a three-day selection centre mapped to CMCV graduate outcomes [“backwards chaining”, 50 ]. This selection centre was designed to allow candidates multiple situations to demonstrate key skills, personal attributes and values. During the selection centre, applicants resided on campus and had much contact with existing students and staff. Over time this intensive and resource-costly process was modified but the basic steps and the core guiding principles remained intact [ 51 , 52 ]:

“I saw the system, and the way they treated me and the group observers [during interviews], how…you know, how much value they give to human values.” [Male, Urban, Corporate Hospital, Class of 80-90s].

The CMCV selection process seemed to be both a structure and tradition, a way of communicating and sustaining CMCV’s values to potential applicants and other stakeholders:

“So, it was very clear that our institution seemed to attract people who had this particular calling to serve others.” [Male, Retired, CMC Medical Education, class of 60-70s].

Reinforcing inherent personal qualities through practices

Values and commitment traits identified during the selection were nurtured and shaped through all aspect of the curriculum [ 53 ]. For example, formal training was aligned with the needs of the communities CMCV serves [resource limited and/or remote and rural settings] and the focus was on preparing the students for those specific working environments in remote and rural communities via early clinical exposure, building clinical skills, and taking on increasing responsibility for patient care:

“We went to these mission hospitals where we were able to see actually what medical practice is in the rural areas where there is dearth of technology, …where we need to be very cost effective and conscious of the resources…. All our teaching and our learning was sort of geared in that direction” [Female, Mission hospital, Rural, class of 80-90s].

Community engagement and expectations

There was a tradition of expecting students to take on social and administrative responsibilities associated with campus living and social service activities [e.g., delivering healthcare of disadvantaged people in the area]. This was designed to help students reach their potential [e.g., in teamworking and leadership skills] and encourage students to actively contribute to the community, thus drawing students into the CMCV community:

“ [Roles in the Hostel Union provide] the opportunity to create budgets- to execute them… gained some experience and knowledge, through all those small post… that we took, helped us later in running mission hospitals” [Male, Mission hospital, Rural, class of 80-90s].

There were also close connections between students and faculty. This was encouraged via a formal programme where students were linked to campus faculty through foster families. However, there were also many social and informal opportunities to mix with and to learn from faculty which helped foster community life:

“It was very personalized. We knew our teachers. We were able to relate to them. We could tell them things. They understood us. They knew us by name. They called us to their homes.” [Male, Mission hospital, Urban, class of 60-70s]. “…I mean there was no kind of a barrier to approach a faculty for any help, be it in studies related or your personal life, wouldn’t matter.” [Male, Urban, Abroad, class 2000-10s].

As mentioned earlier, students were selected for their values in respect of social accountability and the data makes clear that the CMCV values were rooted in social responsibility. Although a predominantly Christian college, students came from different backgrounds and acceptance of difference was encouraged:

“So, we had lots of debates, lots of debates, because we were living together, and so they’d be lot of things, not only about the Bible, also about public work, about what we will go and do ….” [Male, NGO, Rural, class of 70-80s].

Role modelling - seeing others “live it” and by “living it”

Our participants thought the values-driven formal curriculum was enhanced by the role modelling provided by teachers, seniors and mentors. Faculty seemed to inculcate the value of compassion and attending to the needs of others [the students] through their interest and commitment to supporting students, as well as their own medical, academic and personal practices [e.g., opting to deliver service rather than seek high status jobs or salaries elsewhere]. This made a long-lasting impact on many participants who spoke of the role modelling they experienced, reflecting the school’s strong culture of ethical living and patient care at the centre:

“I think that having seen many of my faculty and teachers, the way they handle people, relationships and the importance of maintaining good relationship with people was very helpful… to really value every person” [Male, Mission Hospital Admin, Semiurban, class of 80-90s]. “We could see our seniors especially in the hostel association administration as well as the student’s union how matured and responsible they were and we were able to emulate them as we grew” [Male, Mission hospital, Semiurban, class of 60-70s].

This role modelling seemed to help them internalize CMCV’s values and behave accordingly.

Sustaining practices

Sustaining the traditions.

The communal living with personal, economic and religious diversity, exposure to remote and rural communities early in the training, strong work ethic and taking responsibility for the care and wellbeing of the CMCV community as well as their patients, created a strong sense of group identity and belonging among participants. They discussed how these experiences influenced them, and differentiated them from other medical school graduates in terms of public service and practicing care in the community:

“You should not, you know, use shortcuts to make a quick buck. The things that I took for granted there [CMCV] which when I came here [workplace now], I remember somebody telling me that, you know, what sets you apart is your very strong values that you hold on to” [Female, Private, Urban, class of 60-70s].

In other words, participants described themselves as part of a recognizable group with a clear purpose that differentiated them from graduates from other schools.

Supporting the community [CMCV]

The alumni records from which we drew our sample suggested that a high proportion of CMCV graduates were working in [or had worked in] mission hospitals or NGOs, and/or in rural or semi-urban localities, and drew on what they had learned at CMCV to inform their career and practice decisions. There was a strong sense of bond with the institution which seemed to help CMCV alumni to sustain lifelong learning and get help when working in resource limited settings:

“It is like you become a part of the family of CMC and you never leave… we can always run to this haven of learning and receive so much.” [Female, Mission hospital, Rural, class of 80-90s].

Related to this was the role of the alumni association in maintaining the linkage between the college and its alumni. The alumni association sustained the sense of continuing belonging of the alumni to CMCV through its many activities [e.g., fund raising, reunions]. There was also a tradition of a high level of involvement of alumni within current students [for example, alumni speak to the students about their work, collaborate on teaching and research, and many alumni come back to engage in collaborations and/or work at CMCV], and these alumni communicate and sustain practices; they are a means by which students are educated into CMCV’s practices.

Principal findings

In summary, CMCV had constructed a coherence of culture, structures and processes to pass on their values and traditions to, and through, students who had been selected because they had an inherent disposition towards socially accountable service. During their time in the CMCV community students learned both socially accountable ways of working and a position from which to act in the world. Then, by enacting CMCV virtues in their careers and engaging with CMCV as alumni, CMCV graduates sustained the traditions which provided both practices and individual lives with value.

Strengths and weaknesses of the study

Our study is carried out in the context of one medical school in one country so we cannot assume our findings are generalizable to other contexts. However, the messages from the study are pertinent to all medical schools with a social accountability mission. Our engagement with graduates rather than current students or staff adds to the perspectives already explored in the body of literature on this topic [e.g., 25 , 26 ]. This gave insight into participants’ experiences of a socially accountable education and their views of this post-graduation. Of course, data collection inherently depended on participant recall, but the data suggested that our participants had very clear memories of their time at CMCV. Moreover, the retrospective interview is an accepted method of knowledge construction which can contribute to the understanding of processes in educational practice [ 54 , 55 ].

We do not know if the views and career actions of our interviewees are typical of all CMCV graduates. It may be that those alumni who engaged with the research are those who positively embraced the social accountability values of the institution. There may be other CMCV alumni who did not do so. A large-scale survey of CMCV graduates would be useful, to gather more data on their values and career choices. This data could then be compared with that of graduates from other medical schools set up with social accountability missions elsewhere in India and in other countries. We also suggest the need for future qualitative work comparing the views of alumni from different medical schools, and how they have embraced the social accountability mission of their medical school into their work, would be useful, to discover if what we found is unique to CMC or more widespread. Most of our interviewees had graduated before 2000: more recent graduates may have different views. We have no way of knowing but we suggest that this would be the case in any research of this nature. Future studies may wish to adopt approaches which highlight differences in experience over time and how such differences influence attitudes and work choices.

We used MacIntyre’s theory with its dual emphasis on context, practices and structures, and human characteristics, to aid conceptual generalisability [transferability], not to judge or defend CMCV practices. This theory has its detractors [e.g., 56 ] and obviously, any one theory only illuminates certain aspects of the data [ 57 ]. However, like others [ 41 , 42 , 43 , 44 , 45 ], we found it useful for organising our findings to make clear what was seen as important in terms of developing social accountability by our participants.

Unanswered questions and future research

Students and graduates, the care providers, are only one part of the jigsaw puzzle of social accountability. We suggest that future work examining the translation of social accountable missions to action and future research may wish to engage with patients as well as health care providers and educators.

Our data suggests that social accountability within medical education should be viewed as holistic and complex, dependent on alignment and interactions over time between context and structures, systems and human agents. This opens the door to future research using in-depth qualitative approaches. For example, case study methodology [ 58 ] would enable further, detailed explorations of the relationships and systems related to social accountability within other “tidal pools” [that is, medical schools] and their contexts at different points in time and over time [ 59 ].

Third, socially accountable medical education is an area of much research activity but little use of theory in that research [ 60 ]. We suggest that if “determining whether or not progress is being made in an area of study requires judging …. whether or not the focus of our research efforts continue to evolve” [ 61 , p. 295], our study provides an example of how theory can be used to aid transferability.


In medical education, while acknowledging the reach of the Training for Health Equity Network [THEnet], most published reports of social accountability processes and impacts are from medical schools in high income countries. This reflects general publishing patterns in the field [e.g, 62 ]. In contrast, our study is from a school located in a low/middle income country [LMIC], India, which has long struggled to attract and retain doctors and healthcare professionals to remote, rural and deprived areas. This paper thus brings diversity and practices from a non-Western setting into health professions research and scholarship [ 63 ]. At the same time, our specific research question is relevant to an international audience – the focus on social accountability in health professions education is widespread even if the specific focus of this study was firmly grounded in one school, in one country.

In conclusion, we suggest that the process of engaging students to embody a social accountability mission in their working lives depends on multiple, coherent and interrelated approaches and actions, and the intersection between systems and individuals. We propose that assessing the success of socially accountable medical education is as much about understanding how graduates perceive their education and how that experience influenced how they act within the world, as it is surveying where they work. This information about the reflection and enactment of social accountability can then inform the development of social accountability practices in the future.

Data availability

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

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We thank the colleagues at CMCV’s Alumni Association and CMCV for supporting this research. We thank all those CMCV graduates who took part in the study. We thank Mrs Kiran Devamani for contributing to the preliminary data analysis. We also thank Dr Sara Bhattacharji whose support was invaluable in terms of background information, as well as encouraging engagement with the project.

This study was funded by an Association for Medical Education Europe [AMEE] Grant for Medical Educators working in Resource Constrained Settings [MERCS grant].

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JC and AP conceptualised the study, in discussion with AZ and A[Anna]P. JC led the design of the study, the analysis and interpretation of the data, and the paper writing. SD acquired the participant details, developed the sampling framework and collected the data under supervision from AP and JC. AP conducted the primary data analysis with support from Kiran Devamani [see Acknowledgements] and JC. JC led on the secondary data analysis. All authors contributed to interpreting the data, and the drafting and critical revision of the paper. All authors approved the final manuscript for submission.

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Cleland, J., Zachariah, A., David, S. et al. A qualitative study of social accountability translation: from mission to living it. BMC Med Educ 24 , 145 (2024).

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Challenges and opportunities in strengthening primary mental healthcare for older people in India: a qualitative stakeholder analysis

  • Tom Kafczyk   ORCID: 1 &
  • Kerstin Hämel 1  

BMC Health Services Research volume  24 , Article number:  206 ( 2024 ) Cite this article

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Primary mental healthcare (PMHC) allows for complex mental health issues in old age to be addressed. India has sought to improve PMHC through legislation, strategies and programmes. This study analyses the challenges and opportunities involved in strengthening PMHC for older persons in India from the perspectives of key stakeholders.

Semistructured interviews were conducted with 14 stakeholders selected from the PMHC system in India and analysed using thematic analysis. First, the analysis was organizationally structured in accordance with the six WHO mental health system domains: (1) policy and legislative framework, (2) mental health services, (3) mental health in primary care, (4) human resources, (5) public information and links to other sectors, and (6) monitoring and research. Second, for each building block, challenges and opportunities were derived using inductive coding.

This study highlights the numerous challenges that may be encountered when attempting to strengthen age-inclusive PMHC. Among these challenges are poor public governance, a lack of awareness and knowledge among policy-makers and other stakeholders, and existing policies that make unrealistic promises to weak primary healthcare (PHC) structures with an excessive focus on medicalizing mental health problems. Thus, the mental health system often fails to reach vulnerable older people through PHC. Established approaches to comprehensive, family- and community-oriented PHC support attempts to strengthen intersectoral approaches to PMHC that emphasize mental health promotion in old age. Targeting the PHC workforce through age-inclusive mental health education is considered particularly necessary. Experts further argue that adequate monitoring structures and public spending for mental health must be improved.


In this study, we aim to elaborate on the mental healthcare developments that may serve to achieve equity in access to mental healthcare in India. Coordinated and collaborative efforts by public and private stakeholders involved in the care of older persons, both with and without lived mental health experiences, as well as their families and communities, are necessary to bring the vision of those policies for PMHC to fruition. The findings presented in this study can also inform future research, policies and practice in other low- and middle-income countries.

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Mental healthcare in low- and middle-income countries (LMICs) has received increasing attention in recent years. For example, the Mental Health Action Plan 2013–2020 of the World Health Organization (WHO) presented an important commitment by governments to prioritize mental health in their public policies [ 1 ]. A group that is increasingly recognized as vulnerable to mental health issues in LMICs are older persons [ 2 ]. For example, older persons appear as a vulnerable group in the Indian National Mental Health Policy 2014 [ 3 ]. This recognition relates to the growing awareness of population ageing in LMICs [ 4 , 5 ] and of the complexity of mental health issues in old age [ 6 ].

Data on China, Ghana, India, Mexico, Russia, and South Africa in the Study on Global AGEing and Adult Health (SAGE) as analysed by Arokiasamy et al. [ 7 ] show that, in LMICs, the burden of mental health issues is increasing with age, and the prevalence of mental health impairment is higher in India than in other LMICs. As in most LMICs, among the most prevalent mental health issues in old age in India are depression, anxiety and cognitive impairments [ 6 ]. Patel et al. [ 8 ] found that 17% of persons aged 60 years and older surveyed in the Indian state of Rajasthan had severe depression, 10.3% had anxiety disorders and 51.2% had cognitive impairments.

In relation to the complexity of mental illness in old age, the positive association between the number of chronic physical diseases and mental health issues must be mentioned [ 7 ]. Furthermore, a lower socioeconomic status is associated with lower mental health status in old age [ 9 ]. In LMICs, older persons and households with older family members are often socioeconomically deprived. In India, approximately 66% of older persons live below the poverty line and approximately 73% are illiterate [ 10 ]. In LMICs, social protection systems, such as pensions and health insurance, often fail to provide adequate protection to older persons [ 11 ], placing these individuals at additional risk of mental health issues [ 12 ].

Health system approaches for old-age mental healthcare

Without proper social protection systems, older persons in LMICs often rely on their families for basic necessities [ 13 , 14 ]. Furthermore, the family plays an important caregiving role in LMICs [ 15 ], which is particularly true for India, where traditional values such as filial piety and familialism underpin family care potential [ 15 , 16 ].

The complex and often chronic mental health needs of older people [ 6 ] require multidimensional and diverse responses, including financial, psychological, emotional and social [ 8 ]. Such diverse health system approaches and support structures for the mental health and well-being of older persons are needed to avoid overstraining families and to complement already established formal and informal approaches to care. This becomes especially important as family support for older persons is declining in India due to the erosion of traditional family norms [ 17 ] and to address the increasing number of older persons living alone [ 13 ]. Strong and resilient health systems must focus on the needs and preferences of older persons to address the complex health situations of an ageing population [ 18 ]. A key issue is the mental health needs of older persons [ 18 ], as emphasized in the Madrid International Plan of Action on Ageing endorsed by the United Nations General Assembly in 2002 [ 19 ].

Due to the chronic nature of most mental health issues, health systems should focus on health promotion and disease prevention in addition to long-term care needs [ 18 ]. Furthermore, health services should be close to the homes of older persons [ 20 ] and should be family- and community-oriented [ 18 ]. Primary healthcare (PHC) is seen within this context as a viable, sustainable and strong way forward [ 20 , 21 ]. The Alma-Ata Declaration of 1978, reinforced by the Astana Declaration of 2018, identified PHC as key to the attainment of complete physical, mental and social well-being [ 22 , 23 ]. As the first level of care encountered by individuals– the family and community– the PHC approach builds on equity on the basis of need and individual and community empowerment and participation in the planning and implementation of healthcare [ 22 , 23 , 24 ]. According to Kringos et al. [ 25 ], strong PHC systems incorporate and strengthen the key dimensions of access, continuity, coordination, and comprehensiveness of care. Intersectoral collaboration and interprofessional practices to strengthen and improve PHC services in addressing complex health needs are increasingly emphasized [ 23 , 26 , 27 ].

The integration of mental healthcare at the PHC level, i.e., primary mental healthcare (PMHC), is therefore seen as a critical part of the health system to build a socially just and equitable system that can meet the mental health needs of older persons. Moreover, similar to previous studies on this subject [ 28 , 29 ], we see mental healthcare for older persons as a cross-cutting issue that spans the old-age, mental health and general healthcare sectors, underlining the need for intersectoral collaboration.

The World Health Organization [ 30 ] has summarized each of the key components of a strong mental healthcare system that plays a role in improving mental health. These components are useful to explore the current state, opportunities and challenges of (primary) mental healthcare regarding health system improvements [ 31 ]. We use them as a framework to inform our analysis of the challenges and opportunities in strengthening PMHC for older persons in India. They are defined in Table  1 .

Old-age-inclusive primary mental healthcare (PMHC) in India

In recent years, the need to improve PMHC has been increasingly discussed in Indian society, accompanied by efforts to frame and develop appropriate mental healthcare structures in the public health system. India is in a time of transition towards a broader recognition of the importance of mental healthcare to achieve sustainable development goals [ 33 ]. Consequently, measures are focused on realizing the right to access old-age mental health services. For example, the landmark Indian Mental Healthcare Act (MHCA) 2017 gave older persons the right to old-age mental healthcare [ 28 , 34 ].

Across policies defining the PMHC system in India, such as the National Mental Health Programme [ 35 ] or the National Action Plan and Monitoring Framework for Prevention and Control of Noncommunicable Diseases [ 36 ], four strategies to better address mental healthcare problems in old age have been identified: (1) integrating community health workers (CHWs) into PMHC, (2) empowering the community to participate in healthcare, (3) supporting the family in a family-led approach, and (4) integrating traditional Ayurveda, Yoga, Naturopathy, Unani, Siddha, Sowa-Rigpa and Homeopathy (AYUSH) services into PMHC [ 29 ]. A detailed description of the policies that frame the PMHC system for older persons in India and a contextual description of the architecture of primary mental healthcare (PMHC) in India as envisioned by the policies has been outlined in Kafczyk and Hämel [ 29 ].

However, empirical research on the strengths and weaknesses of PMHC in India and envisioned approaches for older persons is largely lacking. In this context, it is necessary to point out that the PMHC system in India is considered to be developed and transformed by public institutions and private (for- and not-for-profit) organizations involved in the process of defining and implementing national legislation, strategies and programmes. Together, they shape the PMHC system and the system’s ability to decisively provide needs-based mental healthcare to older persons. On the national level in India, four key ministries are defining and proposing the way forward for the PMHC system: the Ministry of Health and Family Welfare (MoH&FW), Ministry of Law and Justice (MoLJ), Ministry of Social Justice and Empowerment (MSJE) and Ministry of Ayurveda, Yoga and Naturopathy, Unani, Siddha and Homeopathy (AYUSH) [ 28 ]. Private and civil society actors participate in the development of general and mental health policies [ 28 ]. Federal-level policies defining the PMHC system call for “[…] the active participation of several stakeholders including civil society, NGOs, academic and research agencies, development partners, the private sector and, most importantly, the community.” [ 37 , pp. 2–3], highlighting the complex multistakeholder approach in India for the strengthening and implementation of PHC and mental healthcare herein.

An in-depth exploration at the country level encompassing the macro and micro-level is needed that incorporates different perspectives within the PMHC system to broaden our understanding of this important subject. A current lack of such studies prevents a deeper understanding of the challenges and opportunities for the PMHC system to address the needs of older persons [ 38 , 39 , 40 ].

Aim of the study

Acknowledging that different stakeholders from the public and private spheres shape the mental health system [ 31 ], this study’s objective is to analyse challenges and opportunities for the anchoring and implementation of PMHC services for older persons in the health system in India. Our analysis is informed by the WHO’s key components that constitute a strong mental health system ranging from policies to practices. This study helps identify strengths and weaknesses to further develop age-inclusive PMHC in India. By identifying leverage points, these findings could guide further research and the design of policies and practices in India and other LMICs.

Study design

A qualitative study design was employed to analyse challenges and opportunities in the anchoring and implementation of PMHC for older persons as perceived by key national stakeholders in the field. We conducted key informant interviews and analysed them using thematic analyses [ 41 ]. The consolidated criteria for reporting qualitative research (COREQ) were followed in the study process [ 42 ].

Sampling and field approach

A key informant (expert) is defined as a person responsible for the development, implementation or control of solutions, strategies, or policies and/or a person with privileged access to policy-level information [ 43 , 44 ] that pertains to mental healthcare for older persons.

An interdisciplinary perspective approach was adopted [ 45 ]. Accordingly, it was our intention to sample different stakeholders involved in framing and developing the (primary) mental health system to represent different perspectives, including federal-level policy-makers or advisors, managers of public and private mental healthcare institutions, representatives from professional psychiatric associations and nongovernmental organizations working on mental health and leading practitioners and researchers on old-age mental healthcare. We believe that our sample goal was reached (see Table  2 ).

To gain entrance into the field and to obtain a sense of whom to interview, a convenience sample was employed. This was followed by purposive sampling, which was informed by snowballing, a review of the literature, conference papers and a web search. The use of purposive sampling ensured that different perspectives, different practice settings, levels of experience and epistemologies from different stakeholders on the study subject were included. Sampling was conducted by the first author in consultation with the second author.

Our sampling was guided by the following criteria: (1) leading experts in the field of mental health, old-age care and/or primary healthcare, (2) over 10 years of working experiences, (3) involved in national-level policy-making processes and/or the implementation of policies, and (4) good command of English. Notably, the experts were not necessarily those in the highest position of their organization but those with the most expertise and knowledge. The final sample size was determined by saturation, which was interpreted as a matter of not numbers but degree [ 46 ].

In total, interviews with 14 key experts from India (5 = female, 9 = male) were conducted and included in this study (see Table  2 ). To be noted is that most experts in this study were active in multiple areas and were often part of the research field [ 44 ].

Key informant interviews

A semi-structured interview guide was developed by the research team (see supplementary material). It covered the following topics: political and public debates and directions in mental healthcare for older persons as well as central challenges and opportunities in PMHC for older persons. The interview-guide consisted of open-ended questions to allow for flexibility during the interviews. However, only through follow-up questions we directed interview partners towards key components of the mental health system, consisting of the policy and legislative framework, mental health within primary care, mental health services, human resources, public education and links with other sectors, and monitoring and research.

To inform the data analysis, at the beginning of each interview, the sociodemographic and professional background of the participants was surveyed. For purposes of anonymity and confidentiality, this information is not presented here. After the first two interviews, the interview guide was reviewed. Since the guide proved to be useful, no adaptations were made. The first author conducted the interviews (median length: 50 min, interquartile range: 26 min). The first author holds a master’s degree in global health, and this study is part of his doctorate studies in public health in Germany (PhD equivalent). Prior to this study, he had worked in the healthcare sector in India. He has 10 years of experience in qualitative research in Germany and LMICs, employing different interview forms and analysis methods. He had no prior relationship with any of the study participants and introduced himself as a researcher. Nonparticipants were not present during interviews. The second author, located in Germany, is the supervisor of the PhD project of the first author, holds a doctorate in social sciences and has comprehensive experience conducting qualitative studies in Germany and abroad for nearly 20 years. All interviews were conducted in English and in person. The language used was English because it allowed the first author (interviewer) to follow the conversation and ask questions, thus facilitating the development of a shared understanding. Challenges posed by a translator could thus be avoided [ 47 ]. However, we are aware that subtleties and nuances may be lost when the interviewer and the interviewee are not using their mother tongue. Overall, however, none of the interviews proved to be markedly difficult in terms of language.

It is important to mention that both authors are of non-Indian origin. Data were collected during the first author’s stay of several months in India. The study was discussed and contextualised with different actors from research and practice in India. Being of non-Indian origin was challenging as there was a distinct lack of familiarity with certain Indian concepts and parts of the health system. This familiarity is often developed by persons who grow up in the study context [ 48 ]. However, we saw this situation as an opportunity that allowed us to be curious with the unfamiliar, ask taboo questions and be seen as nonaligned with certain subgroups or positions [ 48 ]. Since we expected that the non-Indian origin of the primary investigator could influence his positionality on the study subject [ 49 ], we continuously and cautiously reflected on our own assumptions to ensure that they did not impact the interpretation of the data and, ultimately, the study results.

Data collection

Prior to participating in the study, participants received an invitation letter, a subject information sheet and the research was disclosed to them. Hereafter, they either signed an informed consent form or provided verbal consent. Participants were required to give informed consent prior to their participation. Participants were informed that participation was voluntary and they could withdraw from the study at any point without negative consequences.

After consent was obtained from the participants, interviews were recorded, transcribed in their entirety and anonymized. In accordance with applicable data protection rules and regulations and with strict adherence to ethical principles, we maintained the confidentiality of all obtained data. The data were stored on a secured hard drive and were accessible only to the research team.

Interviews were mostly conducted in the offices of the participants ( n  = 10) or in quiet public places ( n  = 4), depending on the participant’s preference. For purposes of confidentiality and privacy, only the interviewer and participant were present during interviews. The interviews took place between 2017 and 2018. This research was delayed because of the coronavirus pandemic.

Data analysis

We analysed the data based on thematic analysis [ 50 , 51 ]. Transcripts were first deductively coded using MAXQDA (VERBI GmbH, Plus 2020) by the first author along the organizational codes of the six WHO components for strengthening (mental) health systems: (1) policy and legislative framework, (2) mental health services, (3) mental health in primary care, (4) human resources, (5) public information and links with other sectors, and (6) monitoring and research [ 32 ]. After the first transcripts were coded, the first author reviewed whether this approach was feasible or whether adaptations needed to be made; after careful consideration, this approach was followed. In the next step, the material within organizational codes was inductively coded using a spreadsheet to identify themes and relationships. This was an iterative process for the research team where content was grouped and regrouped until each theme could be described comprehensively. During this stage, we also regrouped some themes into other organizational codes that better captured the content. The coding was hence a mix of deductive and inductive coding, sometimes referred to as an integrated approach to coding [ 52 ] that organized the data according to its content. We assumed saturation when no new inductively developed themes emerged [ 53 ].

From the data, several themes emerged along the organizational codes and assessment areas of (1) policy and legislative framework, (2) mental health in primary care, (3) mental health services, (4) human resources, (5) public information and links with other sectors, and (6) monitoring and research. These findings are presented in the following. A summary of the organizational codes, themes and initial codes can be found in Table  3 .

Policy and legislative framework

Slow progress in the political agenda setting.

Interviewees described that the mental health of older persons was long out of the focus of federal and state-level policy-makers. Driven by civil society organizations, such as Alzheimer’s and Related Disorders Society of India (ARDSI) or the Indian Psychiatric Society (IPS), experts now see a broader discourse in India and recognition of the need for improving mental healthcare for older persons at the PHC level. The experts note that discussions should be broadened beyond dementia to better address other impairments, such as depression, psychosis or schizophrenia. An increased interest is reflected in policy developments and implementations, such as the formation of the National Institute of Ageing as foreseen in the National Policy on Senior Citizens (2011).

However, in terms of current policies for old-age mental healthcare, experts are conflicted on whether they meet the current needs of older persons. Interviewees perceive policies as not inclusive of old age and mental health, which is attributed partly to a lack of awareness of this issue and a general lack of coordination and collaboration on a federal level. For example, an indifferent or even discriminatory attitude among policy-makers is described by some experts. Furthermore, according to the experts, current policies for older persons are too medical and institutional and not community-oriented, and they fail to see the need for psychosocial interventions. In a similar way, policy

[…] focus is the provisions of basic healthcare, for life style diseases, for cardiac, for joint pains for things like that which are more visible more settling diseases rather than this [mental health, added by the authors]. (E11, Civil society representative, Pos. 43) Footnote 1

This is contested by interviewees outlining that new policies reflect a further evolving mental health field for people in older age, highlighting that policy-makers increasingly recognize the importance of old-age mental healthcare and address these needs, as, for instance, in the National Mental Health Policy.

People are becoming aware that this is a segment which is increasing in number in India now because our life expectancy […]. And they have their own physical and mental health problems. So, the situation has altered very much in favour of them. (E6, Representative of professional association, Pos. 27)

The process is slow, “but it is in the right direction. So, we do see things improving in the near future” (E14, Researcher, Pos. 35). The legislative framework and programs, with the MHCA 2017 and the National Health Programme on Health Care for the Elderly (NPHCE), respectively, mentioned as prominent examples, are described as opportunities to strengthen mental healthcare in the Indian health system, including for older persons. One interview partner optimistically stated,

I think it is a very progressive legislation [the MHCA 2017, added by the authors]. Very progressive in contrast to our conservative society. (E9, Representative of professional association, Pos. 35)

What appears to be supported by study participants to strengthen age-inclusive PMHC is the participation of older persons, interest groups and civil society in policy processes.

[…] older persons are organizing themselves into informal associations […]. So, the need right now is to get together for these old people associations. If they get together, they have a say. And then they can influence government policy, grant making and programs; otherwise […] there is a complete lack of any component of mental healthcare. (E11, Civil society representative, Pos. 88)

Strengthening public health governance for old-age mental healthcare

A major challenge, according to the interviewees, is a lack of accountability and responsibility in the public health system, especially among key ministries. “ Older persons, they keep getting tossed around from here to there. Neither this ministry is doing something for them nor that ministry” (E7, Civil society representative, Pos. 39). For instance, this makes it difficult for civil society organizations to position the topic of old-age-inclusive PMHC on the political agenda. All interviewed experts demanded more political will, recognition and stewardship by the government:

I think political will starting right from the top, the recognition by the health ministry, the commitment by health ministers, health secretaries, and then that filters all the way down the system. The recognition that these are chronic diseases […] are the leading causes of illness and disability in old age. (E2, Researcher, Pos. 46–47)

The expected role of the government to steer and govern should be accompanied by appropriate policies and budgets. Making funding available for mental health for older persons is expected to be challenging, “[…] the [government official] told that it is very difficult to give resources for elderly people” (E8, Representative of government-affiliated advisory agency, Pos. 23). For some, this has to do with “[…] many other things which are competing for the same amount of resources” (E6, Representative of professional association, Pos. 118–121). Furthermore, data are lacking that would allow to monitor resources invested in the mental health of older people.

[…] here in public health systems what is the allocation that is given for mental health? Very small. In that, what percentage is going to the elderly mental health? It is very difficult to find out […] it’s not getting identified, reported. And the consequence […] it is difficult for us to sort of make a case to the government. If data is available, you know, it’s very easy for us to look at the allocation. (E7, Civil society representative, Pos. 17)

The challenges stated above also impact the implementation of policies, which is seen by experts as one of the biggest challenges to better PMHC for older persons. They complain about a lack of implementation capacities at all levels of the Indian health system. For some, this relates to policies that are too idealistic and not drafted in a way to be implementable.

[…] all these policies are fine you know it talks about a vision. […] But it has to come to the ground, it has to be implemented, somebody needs to execute it. (E6, Representative of professional association, Pos. 119)

Development assistance: an opportunity or burden?

Development assistance as an influencing factor of policies and legislative frameworks appeared to be an important discussion point for experts. Development assistance is discussed as a way to learn from other countries and to bring in experiences and technical know-how to support involved ministries and healthcare actors to be more age- and mental health-inclusive in their policies and programmes and to use resources more effectively. Furthermore, international organizations, such as the WHO, are described as important actors that bring certain topics to the public’s attention; as one expert from civil society stated, “ International pressure makes us do a lot of things. […] When WHO tells you to do something you take it a little more seriously and make some effort to show that we do something. […]” (E7, Civil society representative, Pos. 39). Some experts demand more pressure from international bodies.

The overall role of development assistance is contested, however. Of concern to experts is that development actors could skew policy directions because they have their own agendas and are less educated about the current needs in India. One expert who was involved in policy processes outlines that “Development assistance plays a very important role in shaping policy. Most development assistance […] has been damaging to mental health. It has been very harmful in fact because mental health has never been prioritized. […] they are risking skewing our health policy (…)” (E2, Researcher, Pos. 57). This is contrasted by experts who state that India does not rely anymore on development assistance and can choose in which cases they want to work with development actors.

Mental health in primary care

Primary healthcare (phc): too weak to implement the vision of age-inclusive pmhc.

The importance of the PHC level for mental health-inclusive care for older persons is not contested by experts. They describe, among other reasons, the advantages of relieving the pressure of secondary and tertiary care and better access for older persons. However, despite recent government efforts to strengthen PHC, most experts see it as a challenge for older persons to find needs-based help at the PHC level for mental health issues– they usually end up at the hospital level, where specialists are available. This is seen by the interviewees as being strongly connected to lacking capacities at the PHC level to provide mental healthcare. The PHC system is described as weak and struggling with systemic problems such as

[…] desperate shortages of skilled manpower, there are motivational issues […] there is a lack coordination […] If primary healthcare worked with the resources that are already available, I think you would see quite a big change in the quality of care that is being provided. And that would include mental healthcare and care for the elderly. (E2, Researcher, Pos. 33)

To strengthen mental healthcare at the PHC level, the interviewed experts call for “ the integration of mental health training, mental health education, awareness activities at the primary healthcare and community level.” Nevertheless, this “[…] is completely missing” (E13, Civil society representative, Pos. 52). Some experts underline the importance of integrating mental health into existing structures and programmes for old-age care and warn to not develop new structures that are split from other relevant care sectors for older persons, emphasizing an interdisciplinary approach.

It is not only dealing with just the mental health things, but it is also dealing with geriatric understanding and seeing the combination of the two. (E14, Researcher, Pos. 27)

The newly proposed model of Health and Wellness Centres (HWCs) Footnote 2 is linked to optimism in this context among experts as mental health and old-age care services are envisioned to be among the basic services offered here. While the integration of old-age and mental healthcare in HWCs would be “ a distant dream come true to have it” (E3, Pos. 33), experts close to policy processes are sceptical about whether the government’s concept and implementation strategy is realistic and pertinent to old-age-inclusive PMHC.

Family- and community-oriented care as an opportunity

According to the experts, the family plays an important role in caring for older family members with mental health issues and is considered the first level of care: “We still have the family looking after elderly people. That is the biggest positive which has kept us going so far” (E3, Representative of professional association, Pos. 41).

If the family is not able to care for older persons with mental health issues, experts highlight the need for community-based care, specifically CHWs and mid-level care providers, to fill that gap. This becomes crucial given the background of declining informal and familial support structures for older persons in India. However, the response to this situation by the formal care sector is perceived to be too slow; moreover, for older persons without much informal support, it is described that.

[…] there are no formal channels of filling that gap I think that affects their mental well-being a lot. So […] that mental well-being is challenged and there is no response to that, there is no way in which the system tries to identify and respond to it whether formal or informal. (E7, Civil society representative, Pos. 13)

Most experts believe that CHWs could play a critical role in strengthening community-based PMHC with a focus on mental health promotion and in functioning as a connector between informal and formal care through outreach work. In addition, CHWs could support the family in their caregiving role. However, there are different challenges that need to be overcome, such as the already high workload of CHWs and their traditional focus on communicable diseases and maternal and child healthcare.

Integration of traditional health services as a part of PMHC

Traditional health services, specifically AYUSH services, were an often addressed topic by the experts, with most of them pointing out opportunities for PMHC. The interview partners perceived a renewed relevance of AYUSH practices that is related to a push by the government and the media. These practices are highlighted as being more accessible, acceptable and affordable by older persons and are expected to be especially promising for those living in rural areas and/or from lower socioeconomic backgrounds.

Nevertheless, it is also argued that traditional approaches to mental health issues need to be seen with caution, since there “[…] are lot of unprescribed practices which happen to get rid of psychosis or depression or anxiety which are crack-practices. Those are major challenges in the Indian health scenario” (E3, Representative of professional association, Pos. 21).

Mental health services

Needs-based, comprehensive and collaborative care for older persons: a difficult-to-implement vision.

Most interviewees described person-centred and needs-based, interdisciplinary, comprehensive and collaborative care approaches as important principles to facilitate adequate age-inclusive PMHC. For example,

[…] a proper mental health setup will require not just a physician, you will require a counsellor, you will require a psychologist, you will require a social worker, you will require in-house therapy unit, medication, you may require ECT [electroconvulsive therapy, added by the authors] or TMS [transcranial magnetic stimulation, added by the authors] therapy. (E3, Representative of professional association, Pos. 33)

However, there are a number of challenges that result in a “[…] disparity between the needs of the people and what is being provided by the government” (E14, Researcher, Pos. 25), which is particularly pronounced in rural areas. According to the experts, the vision of age-inclusive PMHC is far from becoming a reality on the ground: “Any further improvement will require addressing infrastructural problems, provider issues, those are the system level problems that we need to invest in, not technical fixes” (E2, Researcher, Pos. 41).

Furthermore, a common challenge is that mental health is often seen under a disease paradigm ( “problem or no problem,” E1, Pos. 27), which halts the development of comprehensive approaches to care with mental health promotion and prevention as important pillars.

You know, as a nation, we never realized that mental health is also a part of the overall well-being of the people. So, the focus was more on creating curatives for those illnesses […]. (E13, Patient representative, Pos. 32)

This is seen, for example, in primary care physicians who are not trained in the early detection of mental health issues in older persons. Some experts are pessimistic that this situation will change. To foster comprehensive and collaborative care between different professions, experts demand a stronger integration of the biopsychosocial model of health:

I see a lot of conflicts in terms of mental health. And reason what I think because of multiple terminologies/aetiologies. Like stakeholders are from different areas. For example, people from psychology, they look at mental health in a different way. People from psychiatry, they are biological people. So, they look at mental health in a different way. People from sociology, they look at mental health in a different way. […] So, the multiple stakeholders are there, and they try to classify, try to address with their own understanding. And that is a major issue. If you try to develop some consensus like bio psycho-social model. If you try to integrate it, well a lot of issues can be resolved. (E12, Representative of professional association, Pos. 26–27)

Ultimately, “[…] you have to re-engineer the way primary healthcare itself is organized” (E2, Researcher, Pos. 49), starting from a person-centred perspective acknowledging that the often chronic nature of mental health problems requires a different health system setup.

Private and public mental health services

A common critique of the interviewed experts is that private healthcare providers have gained prominence in mental healthcare: “Unfortunately, India has seen a lot of privatization in the last 20–25 years, which is not the right trend” (E5, Representative of professional association, Pos. 81). According to some of the interview partners, the private sector is mostly looking for financial gains, with higher treatment costs and less emphasis on PHC “[…] because the private sector is not interested in primary healthcare. It’s not a viable area for them” (E13, Civil society representative, Pos. 54). As a consequence, the private sector fails to provide mental health services for the neediest.

However, “if we come to the government level (…) it is miserable” (E1, Patient representative, Pos. 47). Most experts see the need to strengthen the quality of services in the public health sector. Furthermore, it is described that civil society organizations– especially in South India– started to fill care gaps in mental health for older persons and are showing the way with some collaborative approaches between state actors and civil society on a community level. Such collaborative approaches between the state and civil society organizations is seen as a chance to foster age-inclusive PMHC. However, civil society organizations face their own challenges, such as dependency on state actors and difficulties raising funds for older people and mental health, which need to be overcome.

Integrating specialized mental health services for older persons into primary healthcare

Some experts see improvements in specialized services for older persons in recent years with an emphasis on clinical levels that could also have the potential to strengthen primary healthcare.

[…] geriatric clinics have opened in different hospitals which are now providing OPD services and inpatient services where they are dealing with some of these issues [mental health issues, added by the authors]. (E14, Researcher, Pos. 45)

However, the majority underline that “We don’t have any specific services […] for elderly mentally ill population […]” (E5, Representative of professional association, Pos. 49), which is particularly the case in rural areas. According to the experts, this needs to be assessed in the context of a lack of any specialized services for older persons and a general inaccessibility of mental health services “[…] for everyone from childhood to old age” (E2, Researcher, Pos. 23).

Most experts mention psychotherapy for older persons as an example. It is outlined that psychotherapy is often not a priority of service providers and is barely accessible and affordable in the public sector and accepted by older persons. Experts closer to practical levels highlight as a potential asset of Indian mental healthcare the inexpensive accessibility to psychotropic medication for older persons.

Like schizophrenia, depression or say any other serious mental illness. One could easily treat for maybe 4 to 5 to 6 dollars in a month. That would be the cost of medication for most of the illnesses. […] which is definitely very less compared to the cost in other countries. (E5, Representative of professional association, Pos. 71)

Human resources

Shortage of skilled and motivated health workers for old-age-inclusive pmhc.

More pronounced in rural areas, a shortage of skilled and motivated health workers is presented prominently by experts as a challenge to more age-inclusive PMHC.

[…] primary healthcare in India is struggling in many parts of the country. There are desperate shortages of skilled manpower, there are motivational issues, even where there are manpower, they don’t really perform in a kind of way that one would hope. (E2, Researcher, Pos. 33)

This shortage is seen for a wide range of medical and nonmedical professionals. Some experts specifically highlight the shortage of specialists. One participant who is involved in the organization of community-based services for older persons describes:

[…] we have not been able to find enough psychologists or neuropsychologists […] if you look at the number of psychiatrists, number of psychological counsellors and they stop at tertiary level. They never go below that level. […] And to take him or her to a rural area would be quite a challenge because if there is one neuropsychiatrist in one particular hospital you don’t have time to go to a rural area. (E7, Civil society representative, Pos. 21)

This shortage of professionals has been aggravated by brain drain, with many professionals migrating to Europe, the US or Australia, and at the same time, a lack of pulling and retention factors and jobs that attract and keep professionals– especially at the PHC level in rural areas.

A person who is serving in a small say rural area should be given some privileges. Those privileges are not provided […]. So, resources like their living situation should be better, living facilities may be better, as well as salary structure, facilities for treatment. (E5, Representative of professional association, Pos. 85)

Providing age- and mental health-inclusive education to health professionals

According to the experts, another major challenge is that “ Professionals do not have any concrete knowledge about mental health issues of senior citizens” (E11, Civil society representative, Pos. 13). For example, “[…] the primary care physician will not be able to understand that this is a psychiatric disorder” (E9, Representative of professional association, Pos. 51); this can similarly be seen in that “[…] the primary care physicians […] have no knowledge of treating psychiatric disorders. […] they all prescribe alprazolam and diazepam, which are restricted. But when it comes to citalopram or other groups, prescriptions are not many” (E9, Representative of professional association, Pos. 46–47, 49). As a consequence, more cases end up at higher care levels where specialists practice, or the psychosocial problems of older persons and comorbidities are often not treated properly.

Experts demand more age-inclusive education for health professionals that is tailored to the Indian context, so that, for example, psychotherapists “[…] will learn how to deal with older people” (E14, Researcher, Pos. 27) and the potential of human resources can be unlocked. If the educational contents of health professionals, including midline health workers and CHWs, are revised to include the mental health of older persons and to be more in line with a biopsychosocial model of health, human resources are regarded as a substantial opportunity to provide better mental healthcare to older persons by experts. In this context, some experts highlight the need to bring closer together the faculties of geriatrics and psychiatry.

In some part of the country, there is some divisions of geriatric psychiatry. […] those persons who are trained in geriatric psychiatry, of course they can provide more, better services, and they are much more sensitive and aware for geriatric people with mental health illness or issues. (E12, Representative of professional association, Pos. 33–35)

Improving the knowledge of health professionals of mental healthcare for older people is furthermore seen as a way to reduce what is described as a form of discriminatory attitudes towards mental health and old age. For example, mental health issues, especially among older persons, are often not seen as problems that can be treated by health professionals.

I don’t think the primary health physician will […] make a diagnosis of a mental health problem. These are considered avoidable. Why waste my time, that’s what they will say, that I have so much work to […], and then what is this mental health? (E9, Representative of professional association, Pos. 53)

Public information and links with other sectors

Greater public awareness of mental health in old age is required.

Experts notably state a lack of awareness and knowledge among the general public– up to the top of the system– of mental health in old age. This is described as a major obstacle towards an age-inclusive PMHC system and reforms thereto.

There is no public perception […] if you are to go out on the street and ask people what facility would you like the government to open in this country? I am sure nobody will tell you that I want mental health consulting. (E7, Civil society representative, Pos. 39)

Therefore, experts demand effective awareness programmes with clear messages, such as that treating mental health issues in old age helps.

The social protection sector is important to strengthen old-age mental healthcare and empower older persons

Most experts connect the implementation success of PMHC for older persons with the social protection sector. While overall social protection is perceived to improve, there is still a long way to go, according to the interviewees. This is especially important in view of declining informal support structures, a trend that is more pronounced in urban areas.

Pensions are considered important to help create contexts that positively contribute to the mental health of older persons and to make them (financially) more independent of their families, which increases their empowerment. Pensions are described as particularly crucial in the absence of informal support structures.

If I don’t get pension in old age and I am not employed, then where do I go? Who is worried about my mental health? I am worried about my food and meals. So, both survival questions become very important. (E7, Civil society representative, Pos. 45)

While experts acknowledge the presence of some pension schemes, access to adequate public pensions for most older persons is challenging. Despite recent movements to improve pension coverage in India, the topic is perceived by experts to be politically difficult.

And the [government representative, added by the authors] told that it is very difficult to give much resources for elderly people […]; in this government also there is a “no” that they will increase this [for pensions schemes, added by the authors] amount. That is, they are not interested; they are not funding it, like it is a very political and challenging issue. (E8, Representative of government-affiliated advisory agency, Pos. 21–25)

Public health insurance schemes have been introduced relatively recently as a way to finance the health sector. The legal obligation of insurers as outlined in the MHCA 2017 to provide mental healthcare coverage is perceived by the experts as a long-due development that might, in connection with other social welfare programs, represent a chance to improve the mental health of older people and to improve social justice. Nevertheless, some criticize that PMHC is neglected by current schemes that focus on secondary and tertiary care.

Monitoring and research

Lack of research on mental healthcare for older people that can translate into practice.

Regarding research, the experts state a lack of research and data on mental health in old age and appropriate care structures thereto. This lack is more pronounced at lower administrative levels, impedes policy development and the planning and implementation of mental health services at all levels and hinders the monitoring and evaluation of these services and the impact on older persons’ mental health. Therefore, experts closer to the research field suggest investing more time and resources in mental health and mental healthcare research.

At the same time, more public attention needs to be paid to existing research that has been and is being conducted by large, high-level institutions, such as the National Institute of Mental Health and Neurosciences (NIMHANS) or the Institute of Human Behaviour and Allied Sciences (IHBAS).

They are doing very serious research […]. But, how much of their research is taking public attention and for public policy? You need public attention in the geriatric setup. (E7, Civil society representative, Pos. 39)

Health systems able to meet the mental health needs of older persons help reduce disability and the financing burden on health systems and contribute to equity in service delivery [ 18 , 19 ]. In recent national policies in India, such as in the National Mental Health Policy 2014 [ 3 ], older persons have been increasingly acknowledged as a group that is vulnerable to mental health problems [ 28 ]. In India and other LMICs, PMHC is considered a viable way forward to strengthen mental healthcare for older persons and to address the often complex mental health needs in older age [ 20 , 21 ]. In our study, we analysed challenges to and opportunities in anchoring and implementing PMHC for older persons in the health system in India from the perspective of key stakeholders; our analysis was organized along the six key WHO components for strengthening mental health systems: (1) policy and legislative framework, (2) mental health in primary care, (3) mental health services, (4) human resources, (5) public information and links with other sectors, and (6) monitoring and research. From an integrated perspective, we present the main conclusions in the following.

Primary mental healthcare for older persons as a governance issue

Interviewed stakeholders were supportive of what was perceived as a broader discourse in recent years at the federal level on the mental health of older persons and appropriate care structures thereto. However, common areas of criticism demonstrate the need for further changes in the public health system in India to adapt to the demographic and epidemiological transitions [ 28 , 54 ].

Experts continue to see a lack of awareness and knowledge among policy-makers and other stakeholders as an obstacle to courageous progress in strengthening age-inclusive PMHC (similar for other LMICs: Prince, Livingston and Katona [ 2 ]). As it has been suggested earlier, policy-makers and other stakeholders must participate in efforts to raise the understanding of the mental health needs of older persons [ 17 ].

Moreover, the experts in this study perceive positive policy developments such as the MHCA 2017 that continued after data collection took place. Since data collection took place, the National Suicide Prevention Strategy has been enacted [ 55 ], which can be seen as another positive policy development making suicide prevention a public health priority [ 56 ]. Nevertheless, it is unclear how this new strategy contributes to age-inclusive PMHC and warrants further research. Overall, however, the interviewees perceive the actual implementation process as too slow. As shown in this study, major barriers to policy implementation include competing resources, a lack of priority for mental healthcare for older persons, lack of implementation capacities and poor coordination and collaboration among the relevant PMHC actors, or policies that are overly ambitious or not appropriate for implementation. These governance challenges require effective strategies. Petersen et al. [ 57 ] identified strategies to strengthen mental health system governance in six LMICs, including in India. These strategies such as the role clarification of different sectors can be applied to the PMHC system in India. In addition, it can be presumed that PMHC is underfinanced [ 58 , 59 ], which is yet another general barrier to strengthening it. Policy-makers should be aware that a reluctance to increase public spending on mental health casts doubt on establishing a functioning public mental healthcare system (see also: Kafczyk and Hämel [ 29 ]). Philip et al. [ 17 ] recommended distributing mental health budgets in India specifically for old-age-inclusive mental healthcare and for community-based and facility-based interventions to support the idea of PHC of equity on the basis of need [ 24 ].

In consideration of these major structural problems, stakeholders in this study assess that the ambitious policies in India for old-age-inclusive PMHC are not appropriate for implementation. According to the findings of our study, policies should be formulated in a coherent way to enable real changes to follow, e.g., by defining stepwise, short-, middle- and long-term objectives. To foster accountability for the implementation of policies and their progress, adequate monitoring and assessment structures based on transparently available empirical data are needed.

The involvement of mental health service users in policy development and implementation from the top to the bottom of the system has been identified as an important strategy to strengthen inclusive service delivery at the PHC level [ 60 ]. This approach is in line with the priorities of policies in India to empower the community to participate in healthcare planning, implementation and monitoring [ 29 ]. Similarly, experts in this study suggest involving older persons in policy formulations, their implementations and monitoring. In this context, it was positively highlighted that older persons are organizing themselves more in formal associations. However, the general tenor is that the participation of (representatives of) older persons in healthcare planning and implementation is low. This tenor aligns with other findings from India [ 28 ] and other LMICs that have shown that mental health service users are barely involved in strategic decisions [ 60 ]. According to those interviewed for this study, a challenge to the increased participation of older persons is the lack of accountability for mental healthcare for older persons among key ministries. Strong public governance structures for age-inclusive PMHC to enable community empowerment through participation are already incorporated into public policies [ 29 ], but are insufficiently developed.

In our interview study, representatives from civil society organizations also contemplate their shrinking and restrictive space. Partnerships with and contributions from civil society organizations enable the enrichment of community-based service provision and improve service quality in PMHC; moreover, they are an important source of increased awareness for and support of the self-organization of older persons [ 61 ]. Thus, their involvement is advantageous for community empowerment and participation (see: Luisi and Hämel [ 26 ]).

The PMHC system is developed and transformed by different public and private stakeholders [ 28 ]. Therefore, well-balanced, mixed governance is central to the implementation and performance of health sector programmes [ 62 , 63 ] and an intersectoral approach to PMHC, which also helps to integrate old-age mental healthcare into existing structures and approaches. This would also be beneficial to overcoming the vertical nature of numerous parts of the health systems and the top-down approach to policy design and implementation, which was alluded to by the experts and previously criticized [ 29 ]. This complex multistakeholder approach was also discussed by the interviewees. In this respect, they unanimously criticize India’s mental health strategy as being coined by a liberal agenda that supports a free market. The interviewed experts see a large privatization trend. Private, for-profit organizations in particular tend to provide their services in urban areas and dismiss PHC [ 64 ].

In addition, as Kafczyk and Hämel [ 28 ] showed, international actors should be mentioned, as they play an important role in policy developments in India. The potential role of development assistance in fostering age-inclusive PMHC is contested by the experts interviewed for this study. They conclude that development actors support learning exchanges between countries and advocate for more old-age-inclusive policy changes. At the same time, development assistance is described as damaging to domestic priorities, as development actors bring their own agendas that barely include mental health. This argument is supported by evidence from Iemmi [ 65 ], who found that, globally, international donor funding is not well aligned with mental health needs.

Primary healthcare orientation and systemic challenges

Stakeholders in this study emphasized the relevance of PHC in the care for older persons’ mental health. According to them, most cases could be treated at the PHC level in India, which is corroborated by the literature outlining that even complex patient cases can be addressed at the PHC level close to the homes of older persons [ 17 , 66 , 67 ]. However, according to experts, the overall response of the health system to mental health still relies strongly on secondary and tertiary care, and policies are perceived as “medical,” “institutional” and are associated with physical health issues. Furthermore, they have a narrow focus on mother and child care, resulting in a disparity between the needs of older persons and the services being provided. These findings are corroborated by a recent policy analysis on age-inclusive PMHC [ 29 ]. Experts clearly state that PHC suffers from structural problems, including a lack of equipment. Philip et al. [ 17 , p. 136] argued in this context that PMHC for older persons “is practically nonexistent in our country.”

We provide evidence that practitioners refer older persons with mental health problems to higher levels of care, as they lack the capacities to treat such cases. The interplay between PHC and higher levels of care is a known problem in India [ 29 ] that needs to be addressed clearly in policies and in practice, e.g., by implementing a functioning gatekeeping role for physicians and integrating specialists such as psychotherapists at the PHC level [ 13 ].

Moreover, the findings of our study suggest that mental healthcare for older people in general– and PMHC specifically– is hampered by a shortage of qualified staff. This shortage is more pronounced in rural areas and aggravated by brain drain and a lack of pulling factors. There are no incentives for primary care physicians practising in rural areas. Specialists such as psychologists, neuropsychologists, and geriatric psychiatrists are practising at higher levels and in large institutions, but it stops there. This shortage and maldistribution of staff is well known in India [ 61 , 67 , 68 ] and other LMICs [ 2 ]. However, our analyses show that the stakeholders in PMHC perceive human resources as having very large potential in India. A key proposed strategy to unlock this potential is to properly educate health professionals on mental health and mental healthcare for older persons [ 17 , 61 , 67 ]. The curricula for CHWs to specialists need to be reformed to include old-age (mental) healthcare and to bring different fields closer, including geriatric medicine, gerontology and (social) psychiatry and psychology.

Important to consider here is to put some distance from a medical and binary understanding of mental health (i.e., disease and no disease) and move toward a biopsychosocial understanding of (mental) health, which would provide more opportunities to address the social causes of mental health issues in old age and focus on the promotion of mental health. This approach is facilitated by the National Mental Health Policy 2014 [ 29 ]. This understanding of (mental) health would also lay the ground for a common framework for different professions, thus facilitating inter- and multidisciplinary approaches to care and overcoming parallel and top-down approaches to service delivery [ 29 ]. In a study from rural China, the multidisciplinary, team-based approach comprising village physicians, community workers (referred to as “ageing workers”) and psychiatrists was well approved by healthcare providers to provide integrated care, including mental health promotion [ 69 ].

Moreover, age-inclusive training in mental healthcare would provide a way to reduce what stakeholders in this study describe as discriminatory attitudes toward mental health and old age. This is supported by evidence from Koschorke et al. [ 70 ] for four LMICs, including India, that the training of primary care providers is an opportunity to address stigmatizing beliefs and stereotypes.

Integrated care management approaches in PHC for older persons bringing together mental health, old-age and general health services have shown promising results [ 71 ] and have strengthened the continuity of care [ 17 ]; for instance, the above cited study in rural China showed that interdisciplinary teams were able to treat not only mental illnesses but also other comorbid conditions, such as hypertension [ 69 ]. Experts in this study perceived the new model of HWCs as a potential opportunity to provide integrated care for older persons. A recent study came to a similar conclusion but highlighted that there is still a lack of clarity that needs to be addressed regarding the benefits of HWCs for older persons’ mental health [ 29 ].

Integrating traditional care approaches (AYUSH) is perceived as a chance by the interviewed stakeholders, as they are perceived as widely accepted in the older population, particularly in rural regions. These approaches have gained prominence in recent years and are foreseen to be regularly implemented in the proposed HWCs [ 72 ]. For Philip et al. [ 17 ], this is a chance to develop holistic care for the mental health of older persons. Current policies for mental healthcare for older persons follow this strategy as well and foresee the integration of AYUSH services into PHC centres [ 29 ]. Philip et al. [ 17 ] further recommended training AYUSH practitioners in geriatric health. Stakeholders in this study nevertheless raise caution regarding the expansion of AYUSH practices. The quality of AYUSH practices and collaboration with the nontraditional health system must be ensured [ 29 , 67 , 73 ].

An important component of PHC is community-based care, and in this context, stakeholders positively outlined that there are more self-organized groups of and for older persons. For instance, there are groups of older persons who engage in laughter therapy together, which has been shown to be an effective activity to reduce depression symptoms [ 74 , 75 ]. These groups can be seen as a form of peer counselling where people help each other with identifying problems and encouraging each other to adopt healthy lifestyles, leading to improved resilience and social support [ 76 ]. Fostering such peer-support groups is a viable way to indirectly promote mental health and enable social networks that can be integrated into a person’s everyday life [ 13 ].

Since data collection took place for this study, the coronavirus pandemic has stimulated new, especially digital, ways of providing health and mental health services in India. One example is the National Tele-Mental Health Programme (NTMHP), which provides free round-the-clock mental tele-health services to improve access to mental health counselling and care [ 77 ]. It was envisioned that these tele-services would be linked to existing public local mental health services, programmes, and other digital services, such as e-Sanjeevani (a national tele-consultation service) [ 77 ]. These services have the potential to improve age-inclusive PMHC [ 67 ], but there is still a lack of clarity on how and if this is the case and what challenges and opportunities exist for older persons in India.

Finally, the poor overall social protection for older persons in India [ 16 , 17 , 78 ] makes it crucial to foster an equitable, socially just and (age-)inclusive health system, and access to healthcare without financial hardship is seen by some as a part of PHC [ 23 , 79 ]. While this was not a focus of this study, we encourage future studies to investigate social protection more closely and how health financing influences older persons’ access to public and private mental healthcare in India. In addition, the importance of stronger social protections is underlined by studies such as Brinda et al. [ 12 ], who showed a negative association between social protection coverage and the mental health of older persons in LMICs, including India. Another important reason is that the extended family, which often provides a form of social protection to older family members, is breaking down in India [ 17 ].

Limitations of the study and further research needs

We applied a combination of convenience and purposive sampling, which is prone to selection bias [ 80 ]. Other biases may have also interfered with the findings, including interview bias [ 81 ] and social desirability bias [ 82 ]. English is a common language among experts with national-level influence in India, but conducting interviews only in English meant that non-English-speakers were not sampled. We encourage future studies on the subject to also include non-English-speaking experts.

It should be noted that India is a diverse country with extensive internal heterogeneity, even at the PHC level [ 83 ], that is rapidly developing [ 84 ]. While different challenges and opportunities exist in different parts of the country, not least because health is a state matter according to the Indian constitution [ 29 ], we decided to provide an exploratory, aggregated analysis on the macro-level. Further research that takes a closer look at certain administrative and geographic contexts of the country, including the micro-level which has not been a focus of this study, is needed. We employed a qualitative approach, and future analyses could consider quantitative approaches to validate the findings presented here.

The intention of the study was to explore challenges and opportunities from a systems perspective. We included a multistakeholder sample and were able to provide a broad overview of challenges and opportunities to strengthen the PMHC system in India. Nevertheless, the study was biased towards the provider perspective. More research is needed on the ’fit‘ between the PMHC system and older persons’ needs by using, for example, well-established models of accessibility to care [ 85 ]. This would also require including older persons with and without lived experiences with mental health issues, their families, including caregivers, and communities. While we included civil society and patient representative experts in their role as advocates for older persons, this does not replace the inclusion of older persons with and without experiences with mental health issues. Doing so, however, requires a different research approach.

This qualitative study delineates challenges and opportunities in strengthening primary mental healthcare in India for older persons from the perspective of different stakeholders. The study shows several challenges to old-age-inclusive PMHC that range from weak political governance to insufficient primary care structures. Not only are older people affected by these challenges, but their high level of vulnerability often makes them more dependent on affordable, easily accessible services close to their homes. A main asset for strengthening mental healthcare is that such care could be anchored by drawing on established approaches to comprehensive, family- and community-oriented PHC in India. According to the results of our study, PMHC is a viable way forward for India to achieve equity in access to mental healthcare. However, this is going to be a difficult path to take, as there is no robust network of mental health services at the PHC level in India. It is now time to further build on the mental healthcare developments focused on in this study. Despite the study’s focus on India, the findings presented here can also inform further research, policies and practice in other LMICs confronted with population ageing.

Data availability

The qualitative datasets analysed during the current study are not publicly available due to ethical consent restrictions. Upon reasonable request, data extractions can be made available from the corresponding author.

To improve the readability of direct quotes from the interviews, we have linguistically corrected them where appropriate.

Health and Wellness Centres (HWCs) are a key component of ‘Ayushman Bharat– National Health Protection Mission’ and are envisioned as the main mode for the delivery of comprehensive PHC. They are expected to partially replace existing PHC structures, especially Sub Health Centres and Primary Health Centres [ 37 ].


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The authors thank American Journal Experts (AJE) for providing language editing. We further thank the study partners from India for their participation and support in data collection. Our deepest thanks go to the various colleagues from India who provided valuable support in conducting this study. We want to thank Dr. Arun Kumar Tiwari for commenting on the manuscript.

No funding was received from third parties to conduct this study. Open Access funding enabled and organized by Projekt DEAL.

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TK and KH designed the study. TK analysed the interviews, provided a draft interpretation of the results and wrote the first draft. KH reviewed the first draft and was a major contributor to the further interpretation of the data and the writing of the manuscript. All authors read and approved the final manuscript.

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Ethical guidelines were strictly followed throughout the research process. Study participants received an invitation letter and a subject information sheet and either signed an informed consent form or gave their verbal consent. All participants gave their informed consent prior to recording the interview and inclusion in the study. Participation was voluntary. Participants were informed that they could withdraw from the study at any point without any negative consequences. The study protocol was reviewed and approved by the Institutional Ethics Committee, Bielefeld University, Bielefeld, Germany (no. 2017074). The assessment was conducted in accordance with international ethical principles. The methods used in this study were in full accordance with these guidelines.

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TK works for HelpAge Deutschland e. V., a nongovernmental organization (NGO) that supports the inclusion of older women and men in services and policies in LMICs. This article is based on his PhD studies at Bielefeld University. TK declares that the statements in this article are independent from the opinion of HelpAge. Moreover, the research team continuously reflected on their own assumptions and goals as well as the constructed nature of the research and cautiously ensured that they did not impact the study. KH declares no competing interests.

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Kafczyk, T., Hämel, K. Challenges and opportunities in strengthening primary mental healthcare for older people in India: a qualitative stakeholder analysis. BMC Health Serv Res 24 , 206 (2024).

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Received : 09 January 2023

Accepted : 21 January 2024

Published : 15 February 2024


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Keywords (MeSH terms)

  • Health Services for the Aged
  • Mental Health
  • Primary Health Care
  • Qualitative Research
  • Health Personnel
  • General Practice

BMC Health Services Research

ISSN: 1472-6963

inductive analysis of data


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    Deductive , or a priori, analysis generally means applying theory to the data to test the theory. It's a kind of "top-down" approach to data analysis. In qualitative analysis, this often means applying predetermined codes to the data.

  2. A General Inductive Approach for Analyzing Qualitative Evaluation Data

    The purposes for using an inductive approach are to (a) condense raw textual data into a brief, summary format; (b) establish clear links between the evaluation or research objectives and the summary findings derived from the raw data; and (c) develop a framework of the underlying structure of experiences or processes that are evident in the raw...

  3. Inductive vs. Deductive Research Approach

    Inductive research approach When there is little to no existing literature on a topic, it is common to perform inductive research, because there is no theory to test. The inductive approach consists of three stages: Observation A low-cost airline flight is delayed Dogs A and B have fleas Elephants depend on water to exist Seeking patterns

  4. A General Inductive Approach for Qualitative Data Analysis

    The purposes for using an inductive approach are to (1) to condense extensive and varied raw text data into a brief, summary format; (2) to establish clear links between the research objectives...

  5. Inductive Reasoning

    Revised on June 22, 2023. Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It's usually contrasted with deductive reasoning, where you go from general information to specific conclusions. Inductive reasoning is also called inductive logic or bottom-up reasoning. Note

  6. Inductive Content Analysis

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  8. What is Inductive Reasoning?

    Inductive reasoning is an analytical approach that involves proposing a broader theory about the research topic based on the data that you use in your study. Inductive reasoning is a bottom-up approach where researchers construct knowledge and propose new theory that emerges from the data.

  9. PDF A general inductive approach for qualitative data analysis

    The inductive approach is a systematic procedure for analysing qualitative data where the analysis is guided by specific objectives. The primary purpose of the inductive approach is to allow research findings to emerge from the frequent, dominant or significant themes inherent in raw data, without the restraints imposed by structured methodologies.

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    Inductive content analysis (ICA), or qualitative content analysis, is a method of qualitative data analysis well-suited to use in health-related research, particularly in relatively small-scale, non-complex research done by health professionals undertaking research-focused degree courses.

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    Inductive analysis is a process of coding the data without trying to fit it into a preexisting coding frame or the researcher's analytic preconceptions. In this sense, this form of thematic analysis is data-driven ( Braun & Clarke, 2006 ).

  13. Inductive Content Analysis & Deductive Content Analysis in ...

    In qualitative content analysis, there are three ways to isolate your data: through inductive content analysis which starts by examining the data, deductive content analysis which organizes data based on pre-existing ideas and research, or a by using a combination of both approaches.

  14. PDF A General Inductive Approach for Analyzing Qualitative Evaluation Data

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    Inductive analysis, on the other hand, is a more emergent strategy, where the researcher reads through the data and allows codes to emerge/names concepts as they emerge. It's more of a "bottom-up" analytic strategy.

  16. Inductive and deductive approaches to research

    The main difference between inductive and deductive approaches to research is that whilst a deductive approach is aimed and testing theory, an inductive approach is concerned with the generation of new theory emerging from the data. ... The aim is to generate a new theory based on the data. Once the data analysis has been completed the ...

  17. Inductive Approach (Inductive Reasoning)

    Generally, the application of inductive approach is associated with qualitative methods of data collection and data analysis, whereas deductive approach is perceived to be related to quantitative methods. The following table illustrates such a classification from a broad perspective:

  18. PDF Theoretical Views and Inductive Data Analysis

    non-parametric. discrete data-analytic methods (that is, methods of 'combinatorial data analysis') were refined, and now enjoy increasing popularity (see, e.g., Arabie and Hubert. 1992; Arabie, Hubert and De Soete. in press; Guenoche and Monjardet, 1987). The emergence of the field of anificial intelligence has brought a new

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    The question of analysis in mixed methods research is an important topic of contemporary debate. For example, Onwuegbuzie and Johnson (2021) note "data analysis in mixed methods research [can be]…the most difficult step of the mixed methods research process" (p. 1) and there is a "lack of methodological guidance in the extant literature on these topics" (p. 16).

  20. How to Do Thematic Analysis

    Different approaches to thematic analysis. Once you've decided to use thematic analysis, there are different approaches to consider. There's the distinction between inductive and deductive approaches:. An inductive approach involves allowing the data to determine your themes.; A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there ...

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    Data management and analysis. Participant interviews were iterative and digitally audio-recorded, transcribed verbatim by a third party approved by CMCV, and anonymised during transcription. In keeping with our constructivist stance, initial data analysis was inductive, thematic and reflexive [33, 34]. SD and AP carried out the initial analysis ...

  23. A Practical Iterative Framework for Qualitative Data Analysis

    Simply put, "Inductive analysis means that the patterns, themes, and categories of analysis come from the data; they emerge out of the data rather than being imposed on them prior to data collection and analysis" ( Patton, 1980, p. 306). From our experience, however, patterns, themes, and categories do not emerge on their own.

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    Background Primary mental healthcare (PMHC) allows for complex mental health issues in old age to be addressed. India has sought to improve PMHC through legislation, strategies and programmes. This study analyses the challenges and opportunities involved in strengthening PMHC for older persons in India from the perspectives of key stakeholders. Methods Semistructured interviews were conducted ...

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    In inductive (or data-driven) coding the codes are derived from the real data, whereas in deductive (or theory-driven) coding in thematic analysis the codes are derived from theory. Naeem and Ozuem's (2022a) study involved both deductive and deductive approach to thematic analysis where the coding and theme development were grounded in pre ...