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Thematic Analysis – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

Thematic analysis is one of the most important types of analysis used for qualitative data . When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the speaker.

Moreover, with the help of this analysis, data can be simplified.  

Importance of Thematic Analysis

Thematic analysis has so many unique and dynamic features, some of which are given below:

Thematic analysis is used because:

• It is flexible.
• It is best for complex data sets.
• It is applied to qualitative data sets.
• It takes less complexity compared to other theories of analysis.

Intellectuals and researchers give preference to thematic analysis due to its effectiveness in the research.

How to Conduct a Thematic Analysis?

While doing any research , if your data and procedure are clear, it will be easier for your reader to understand how you concluded the results . This will add much clarity to your research.

Understand the Data

This is the first step of your thematic analysis. At this stage, you have to understand the data set. You need to read the entire data instead of reading the small portion. If you do not have the data in the textual form, you have to transcribe it.

Example: If you are visiting an adult dating website, you have to make a data corpus. You should read and re-read the data and consider several profiles. It will give you an idea of how adults represent themselves on dating sites. You may get the following results:

I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humor. Being a handyperson, I keep busy working around the house, and I also like to follow my favourite hockey team on TV or spoil my two granddaughters when I get the chance!! Enjoy most music except Rap! I keep fit by jogging, walking, and bicycling (at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times, and adventures together

I enjoy photography, lapidary & seeking collectibles in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception.

Development of Initial Coding:

At this stage, you have to do coding. It’s the essential step of your research . Here you have two options for coding. Either you can do the coding manually or take the help of any tool. A software named the NOVIC is considered the best tool for doing automatic coding.

For manual coding, you can follow the steps given below:

• Please write down the data in a proper format so that it can be easier to proceed.
• Use a highlighter to highlight all the essential points from data.
• Make as many points as possible.
• Take notes very carefully at this stage.
• Apply themes as much possible.
• Now check out the themes of the same pattern or concept.
• Turn all the same themes into the single one.

Example: For better understanding, the previously explained example of Step 1 is continued here. You can observe the coded profiles below:

Make Themes

At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.

Extracted Data Review

Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.

For better understanding, a mind-mapping example is given here:

Reviewing all the Themes Again

You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation. 

You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.

Corpus Data

Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.

When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:

Define all the Themes here

Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.

The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.

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Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.

Make a Report

You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.

While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.  

Frequently Asked Questions

What is meant by thematic analysis.

Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.

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How to do a thematic analysis

What is a thematic analysis?

When is thematic analysis used, braun and clarke’s reflexive thematic analysis, the six steps of thematic analysis, 1. familiarizing, 2. generating initial codes, 3. generating themes, 4. reviewing themes, 5. defining and naming themes, 6. creating the report, the advantages and disadvantages of thematic analysis, disadvantages, frequently asked questions about thematic analysis, related articles.

Thematic analysis is a broad term that describes an approach to analyzing qualitative data . This approach can encompass diverse methods and is usually applied to a collection of texts, such as survey responses and transcriptions of interviews or focus group discussions. Learn more about different research methods.

A researcher performing a thematic analysis will study a set of data to pinpoint repeating patterns, or themes, in the topics and ideas that are expressed in the texts.

In analyzing qualitative data, thematic analysis focuses on concepts, opinions, and experiences, as opposed to pure statistics. This requires an approach to data that is complex and exploratory and can be anchored by different philosophical and conceptual foundations.

A six-step system was developed to help establish clarity and rigor around this process, and it is this system that is most commonly used when conducting a thematic analysis. The six steps are:

• Familiarization
• Generating codes
• Generating themes
• Reviewing themes
• Defining and naming themes
• Creating the report

It is important to note that even though the six steps are listed in sequence, thematic analysis is not necessarily a linear process that advances forward in a one-way, predictable fashion from step one through step six. Rather, it involves a more fluid shifting back and forth between the phases, adjusting to accommodate new insights when they arise.

And arriving at insight is a key goal of this approach. A good thematic analysis doesn’t just seek to present or summarize data. It interprets and makes a statement about it; it extracts meaning from the data.

Since thematic analysis is used to study qualitative data, it works best in cases where you’re looking to gather information about people’s views, values, opinions, experiences, and knowledge.

Some examples of research questions that thematic analysis can be used to answer are:

• What are senior citizens’ experiences of long-term care homes?
• How do women view social media sites as a tool for professional networking?
• How do non-religious people perceive the role of the church in a society?
• What are financial analysts’ ideas and opinions about cryptocurrency?

To begin answering these questions, you would need to gather data from participants who can provide relevant responses. Once you have the data, you would then analyze and interpret it.

Because you’re dealing with personal views and opinions, there is a lot of room for flexibility in terms of how you interpret the data. In this way, thematic analysis is systematic but not purely scientific.

A landmark 2006 paper by Victoria Braun and Victoria Clarke (“ Using thematic analysis in psychology ”) established parameters around thematic analysis—what it is and how to go about it in a systematic way—which had until then been widely used but poorly defined.

Since then, their work has been updated, with the name being revised, notably, to “reflexive thematic analysis.”

One common misconception that Braun and Clarke have taken pains to clarify about their work is that they do not believe that themes “emerge” from the data. To think otherwise is problematic since this suggests that meaning is somehow inherent to the data and that a researcher is merely an objective medium who identifies that meaning.

Conversely, Braun and Clarke view analysis as an interactive process in which the researcher is an active participant in constructing meaning, rather than simply identifying it.

The six stages they presented in their paper are still the benchmark for conducting a thematic analysis. They are presented below.

This step is where you take a broad, high-level view of your data, looking at it as a whole and taking note of your first impressions.

This typically involves reading through written survey responses and other texts, transcribing audio, and recording any patterns that you notice. It’s important to read through and revisit the data in its entirety several times during this stage so that you develop a thorough grasp of all your data.

After familiarizing yourself with your data, the next step is coding notable features of the data in a methodical way. This often means highlighting portions of the text and applying labels, aka codes, to them that describe the nature of their content.

In our example scenario, we’re researching the experiences of women over the age of 50 on professional networking social media sites. Interviews were conducted to gather data, with the following excerpt from one interview.

In the example interview snippet, portions have been highlighted and coded. The codes describe the idea or perception described in the text.

It pays to be exhaustive and thorough at this stage. Good practice involves scrutinizing the data several times, since new information and insight may become apparent upon further review that didn’t jump out at first glance. Multiple rounds of analysis also allow for the generation of more new codes.

Once the text is thoroughly reviewed, it’s time to collate the data into groups according to their code.

Now that we’ve created our codes, we can examine them, identify patterns within them, and begin generating themes.

Keep in mind that themes are more encompassing than codes. In general, you’ll be bundling multiple codes into a single theme.

To draw on the example we used above about women and networking through social media, codes could be combined into themes in the following way:

You’ll also be curating your codes and may elect to discard some on the basis that they are too broad or not directly relevant. You may also choose to redefine some of your codes as themes and integrate other codes into them. It all depends on the purpose and goal of your research.

This is the stage where we check that the themes we’ve generated accurately and relevantly represent the data they are based on. Once again, it’s beneficial to take a thorough, back-and-forth approach that includes review, assessment, comparison, and inquiry. The following questions can support the review:

• Has anything been overlooked?
• Are the themes definitively supported by the data?
• Is there any room for improvement?

With your final list of themes in hand, the next step is to name and define them.

In defining them, we want to nail down the meaning of each theme and, importantly, how it allows us to make sense of the data.

Once you have your themes defined, you’ll need to apply a concise and straightforward name to each one.

In our example, our “perceived lack of skills” may be adjusted to reflect that the texts expressed uncertainty about skills rather than the definitive absence of them. In this case, a more apt name for the theme might be “questions about competence.”

To finish the process, we put our findings down in writing. As with all scholarly writing, a thematic analysis should open with an introduction section that explains the research question and approach.

This is followed by a statement about the methodology that includes how data was collected and how the thematic analysis was performed.

Each theme is addressed in detail in the results section, with attention paid to the frequency and presence of the themes in the data, as well as what they mean, and with examples from the data included as supporting evidence.

The conclusion section describes how the analysis answers the research question and summarizes the key points.

In our example, the conclusion may assert that it is common for women over the age of 50 to have negative experiences on professional networking sites, and that these are often tied to interactions with other users and a sense that using these sites requires specialized skills.

Thematic analysis is useful for analyzing large data sets, and it allows a lot of flexibility in terms of designing theoretical and research frameworks. Moreover, it supports the generation and interpretation of themes that are backed by data.

There are times when thematic analysis is not the best approach to take because it can be highly subjective, and, in seeking to identify broad patterns, it can overlook nuance in the data.

What’s more, researchers must be judicious about reflecting on how their own position and perspective bears on their interpretations of the data and if they are imposing meaning that is not there or failing to pick up on meaning that is.

Thematic analysis offers a flexible and recursive way to approach qualitative data that has the potential to yield valuable insights about people’s opinions, views, and lived experience. It must be applied, however, in a conscientious fashion so as not to allow subjectivity to taint or obscure the results.

The purpose of thematic analysis is to find repeating patterns, or themes, in qualitative data. Thematic analysis can encompass diverse methods and is usually applied to a collection of texts, such as survey responses and transcriptions of interviews or focus group discussions. In analyzing qualitative data, thematic analysis focuses on concepts, opinions, and experiences, as opposed to pure statistics.

A big advantage of thematic analysis is that it allows a lot of flexibility in terms of designing theoretical and research frameworks. It also supports the generation and interpretation of themes that are backed by data.

A disadvantage of thematic analysis is that it can be highly subjective and can overlook nuance in the data. Also, researchers must be aware of how their own position and perspective influences their interpretations of the data and if they are imposing meaning that is not there or failing to pick up on meaning that is.

How many themes make sense in your thematic analysis of course depends on your topic and the material you are working with. In general, it makes sense to have no more than 6-10 broader themes, instead of having many really detailed ones. You can then identify further nuances and differences under each theme when you are diving deeper into the topic.

Since thematic analysis is used to study qualitative data, it works best in cases where you’re looking to gather information about people’s views, values, opinions, experiences, and knowledge. Therefore, it makes sense to use thematic analysis for interviews.

After familiarizing yourself with your data, the first step of a thematic analysis is coding notable features of the data in a methodical way. This often means highlighting portions of the text and applying labels, aka codes, to them that describe the nature of their content.

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Thematic analysis part 1: introduction to the topic and an explanation of ‘themes’

Posted on 21st February 2020 by Dolly Sud

This is the first of a three-part blog which will provide an introduction to Thematic analysis and discussion of what a theme is (part 1), a description of the three schools of TA and some study design recommendations (part 2), and an outline of the six phases of reflexive TA (part 3). A list of key reference sources is also provided.

Introduction

There is an array of methods available to researchers that can be used to identify patterned meaning across a dataset. Thematic analysis (TA) is one of these and is a widely embraced method for analysing qualitative data to inform many different research questions across a wide range of disciplines. It can be used for a variety of types of datasets and applied in a variety of different ways, thus, demonstrating its flexibility. Importantly, it is a very accessible method for novice researchers.

TA is an umbrella term that describes approaches which are aimed at identifying patterns (“themes”) across qualitative datasets [1,2]. It should not be considered to be a single qualitative analytic approach [1] and neither should it be considered a methodology – it is a method .

Victoria Clarke and Virginia Braun are authors of the most widely cited resources on TA – the content of this blog is based on information available on their website and published papers [1,2,3].

Take-home messages:

• thematic analysis is a method not a methodology
• thematic analysis should not be considered to be a single qualitative analytic approach

What is a theme?

There are two conceptualizations of themes which are articulated in the literature [2]:

1. Shared meaning based patterns

Shared meaning based patterns are organised around a central organising concept (core concept). In one of the online lectures [4] available for TA this is likened to a dandelion spherical seed head containing many single-seeded fruits. The seed head being the central organising concept, and the fruits being the themes.

Themes are built from smaller units known as codes.

Shared meaning based patterns [2]:

• capture the essence and spread of meaning;
• unite data that might otherwise appear disparate, or meaning that occurs in multiple and varied contexts;
• they (often) explain large portions of a dataset;
• are often abstract entities or ideas, capturing implicit ideas “beneath the surface” of the data, but can also capture more explicit and concrete meaning.

Braun & Clarke view themes as being shared meaning based patterns.

A good way of understanding the idea of themes is to look at published [2] examples of good TA (a full reference list is available on the website [5]).

Examples of themes as shared meaning based patterns taken from a paper which sought to explore anorexia nervosa clients’ perceptions of their therapists’ body [6]:

• “Wearing eating disorder glasses,”
• “You’re making all sorts of assumptions as a client,”
• “Appearance matters.”

2. Domain summary [2]

This conceptualisation is in contrast to that of a theme as shared meaning based patterns. It summarizes what participants said in relation to a topic or issue, typically at the semantic or surface level of meaning, and usually reports multiple or even contradictory meaning content. The issues are often based around data collection tools, such as responses to a particular interview question.

Example of themes as domain summary from a paper on Muslim views on mental health and psychotherapy [7], the seven themes were outlined as follows:

• “problem management,”
• “relevance of services,”
• “barriers,”
• “service delivery,”
• “therapy content,”
• “therapist characteristics”

You can see that domain summaries don’t appear to consider shared meaning or differences.

A useful pointer here is to consider domain summaries as collecting data under headings which are often composed of single words. Whereas shared meaning based patterns seek to unite data.

Take-home message:

• domain summaries and shared meaning-based patterns, although both articulated as being themes in published literature, are not the same thing.

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Thematic analysis part 3: six phases of reflexive thematic analysis

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• How to Do Thematic Analysis | Guide & Examples

How to Do Thematic Analysis | Guide & Examples

Published on 5 May 2022 by Jack Caulfield . Revised on 7 June 2024.

Thematic analysis is a method of analysing 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:

• Familiarisation
• Generating themes
• Reviewing themes
• Defining and naming themes

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: familiarisation, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up.

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 secondary school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyse it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large datasets 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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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.

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

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

After you’ve decided thematic analysis is the right method for analysing 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 analysing 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 colours 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 condensed overview of the main points and common meanings that recur throughout the data.

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 dataset 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.

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A Comprehensive Guide to Thematic Analysis in Qualitative Research

What is Qualitative Data?

What do all the methods above have in common? They result in loads of qualitative data. If you're not new here, you've heard us mention qualitative data many times already. Qualitative data is non-numeric data that is collected in the form of words, images, or sound bites. Qual data is often used to understand people's experiences, perspectives, and motivations, and is often collected and sorted by UX Researchers to better understand the company's users. Qualitative data is subjective and often in response to open-ended questions, and is typically analyzed through methods such as thematic analysis, content analysis, and discourse analysis. In this resource we'll be focusing specifically on how to conduct an effective thematic analysis from scratch! Qualitative data is the sister of quantitative data, which is data that is collected in the form of numbers and can be analyzed using statistical methods. Qualitative and quantitative data are often used together in mixed methods research, which combines both types of data to gain a more comprehensive understanding of a research question.

UX Research Methods

There are many different types of UX research methods that can be used to gather insights about user behavior and attitudes. Some common UX research methods include:

• Interviews: One-on-one conversations with users to gather detailed information about their experiences, needs, and preferences.
• Surveys: Online or paper-based questionnaires that can be used to gather large amounts of data from a broad group of users.
• Focus groups: Group discussions with a moderated discussion to explore user attitudes and behaviors.
• User testing: Observing users as they interact with a product or service to identify problems and gather feedback.
• Ethnographic research: Observing and interacting with users in their natural environments to gain a deep understanding of their behaviors and motivations.
• Card sorting: A technique used to understand how users categorize and organize information.
• Tree testing: A method used to evaluate the effectiveness of a website's navigation structure.
• Heuristic evaluation: A method used to identify usability issues by having experts review a product and identify potential problems.
• Expert review: Gathering feedback from industry experts on a product or service to identify potential issues and areas for improvement.

Introduction to Thematic Analysis of Qualitative Data

Thematic analysis is a popular way of analyzing qualitative data, like transcripts or interview responses, by identifying and analyzing recurring themes (hence the name!). This method often follows a six-step process, which includes getting familiar with the data, sorting and coding the data, generating your various themes, reviewing and editing these themes, defining and naming the themes, and writing up the results to present. This process can help researchers avoid confirmation bias in their analysis. Thematic analysis was developed for psychology research, but it can be used in many different types of research and is especially prevalent in the UX research profession.

When to Use Thematic Analysis

Thematic analysis is a useful method for analyzing qualitative data when you are interested in understanding the underlying themes and patterns in the data. Some situations in which thematic analysis might be appropriate include:

• When you have a large amount of qualitative data, such as transcripts from interviews or focus groups.
• When you want to understand people's experiences, perspectives, or motivations in depth.
• When you want to identify patterns or themes that emerge from the data.
• When you want to explore complex and open-ended research questions.
• When you are interested in understanding how people make sense of their experiences and the world around them.

Some UX research specific questions that could be a good fit for thematic analysis are:

• How do users think about their experiences with a particular product, service or company?
• What are the common challenges that a user might encounter when using a product or service, and how do they overcome them?
• How do users make sense of the navigation of a website or app?
• What are the key drivers of user satisfaction or dissatisfaction with a product or service?
• How do users' experiences with a product or service compare with their expectations?

It is important to keep in mind that thematic analysis is just one of many methods for analyzing qualitative data, and it may not be the most appropriate method for every research question or situation. A key part of a UX researcher's role is being aware of the most appropriate research method to use based on the problem the company is trying to solve and the constraints of the company's research practice.

Types of Thematic Analysis

There are two primary types of thematic analysis, called inductive and deductive approaches. An inductive approach involves going into the study blind, and allowing the results of the data-capture to guide and shape the analysis and theming. Think of it like induction heating-- the data heats your results! (OK, we get it, that was a bad joke. But you won't forget now!) An example of an inductive approach would be parachuting onto a client without knowing much about their website, and discovering the checkout was difficult to use by the amount of people who brought it up. An easy theme! On the flip-side, a deductive approach involves attacking the data with some preconceived notions you expect to find in the qualitative data, based on a theory. For example, if you think your company's website navigation is hard to use because the text is too small, you may find yourself looking for themes like "small text" or "difficult navigation." We don't have a joke for this one, but we tried. To get even more nitty-gritty, there are two additional types of thematic analysis called semantic and latent thematic analysis. These are more advanced, but we'll throw them here for good measure. Semantic thematic analysis involves identifying themes in the data by analyzing the exact wording of the comments made used by participants. Latent thematic analysis involves identifying themes in the data by analyzing the underlying meanings and actions that were taken, but perhaps not necessarily stated by study participants. Both of these methods can be used in user research, though latent analysis is more popular because users often say different things than what they actually do.

Steps in Conducting a Thematic Analysis

Let's jump in! As mentioned before, there are 6 steps to completing a thematic analysis.

Step One: get familiar with your data!

This might seem obvious, but sometimes it's hard to know when to start. This might take the form of listening to the audio interviews or unmoderated studies, or reading the notes taken during a moderated interview. It's important to know the overall ideas of what you're dealing with to effectively theme your study. While you're doing this, pay attention to some big picture themes you can use in step two when you code your data. Break out key ideas from each participant. This might take the form of summarized answers for each question response, or a written review of actions taken for each task given. Just make sure to standardize it across participants.

Step Two: sort & code the data.

Now that you have your standardized notes across your participants, it's time to sort and code the collected qualitative data! Think of the themes from before when you were taking your notes. Think of these codes like metaphorical buckets, and start sorting! Every comment that fits a theme in a box, put it there. Back to our navigation example: some codes could be "small text" or "hard to use." We could put a participant action of "squinting" into the bucket for "small text," or a comment from another mentioning they had trouble finding "tents" in "hard to use."

Step Three: break the codes into themes!

Try to think of each theme as a makeup of three or more codes. For the navigation example, we could put both "small text," and "hard to use" into a theme of "Difficult Navigation."

Step Four: review and name your themes.

Now is the time to clean up the data. Are all your themes relevant to the problem you're trying to solve? Are all the themes coherent and straightforward? Are you comfortable defending your theme choices to teammates? These are all great questions to ask yourself in this stage.

Step Five: Present!!

To have a cohesive presentation of your thematic analysis, you'll need to include an introduction that explains the user problem you were trying to identify and the method you took to study it. Use the terminology from beginning of this resource to identify your research method. Usually for something like this, it will be a user survey or interview. ‍ You also need to include how you analyzed your participant data (inductive, deductive, latent or semantic) to identify your codes and themes. In the meaty section of your presentation, describe each theme and give quotations and user actions from the data to support your points.

Step Six: Insights and Recommendations

Your conclusion should not stop at your presentation of your findings. The best user researchers are valuable for both their insights and recommendations. Since UX researchers spend so much time with participants, they have indispensable knowledge about the best way to do things that make life easy for the company's users. Don't keep this information to yourself! On the final 1-3 slides of your presentation, state the "Next Steps & Recommendations" that you'd like your team and leadership to follow up on. These recommendations could include things like additional qualitative or quantitative studies, UX changes to make or test, or a copy change to make the experience clearer for readers. Your ultimate job is to create the best user experience, and you made it this far-- you got this!

And there you have it! That's everything you need to complete a thematic analysis of qualitative data to identify potential solutions or key concepts for a particular user problem. But don't stop there! We recommend using these principles in the wild to conduct research of your own. Identify a question or potential problem you'd like to analyze on one of your favorite sites. Use a service like Sprig to come up with non-bias questions to ask friends and family to try and gather your own qualitative data. Next, complete and document yourself completing the 6-step analysis process. What do you discover? Be prepared to share on interviews-- hiring managers love to see initiative! Good luck.

View the UX Research Job Guide Here

Our Sources: 

Caulfield, J. (2022, November 25). How to Do Thematic Analysis | Step-by-Step Guide & Examples . Scribbr. https://www.scribbr.com/methodology/thematic-analysis/

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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22 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

For qualitative studies, can the findings be structured according to the Research questions? Thank you.

Do I need to include literature/references in my findings chapter?

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Presenting your qualitative analysis findings: tables to include in chapter 4.

The earliest stages of developing a doctoral dissertation—most specifically the topic development  and literature review  stages—require that you immerse yourself in a ton of existing research related to your potential topic. If you have begun writing your dissertation proposal, you have undoubtedly reviewed countless results and findings sections of studies in order to help gain an understanding of what is currently known about your topic. 

In this process, we’re guessing that you observed a distinct pattern: Results sections are full of tables. Indeed, the results chapter for your own dissertation will need to be similarly packed with tables. So, if you’re preparing to write up the results of your statistical analysis or qualitative analysis, it will probably help to review your APA editing  manual to brush up on your table formatting skills. But, aside from formatting, how should you develop the tables in your results chapter?

In quantitative studies, tables are a handy way of presenting the variety of statistical analysis results in a form that readers can easily process. You’ve probably noticed that quantitative studies present descriptive results like mean, mode, range, standard deviation, etc., as well the inferential results that indicate whether significant relationships or differences were found through the statistical analysis . These are pretty standard tables that you probably learned about in your pre-dissertation statistics courses.

But, what if you are conducting qualitative analysis? What tables are appropriate for this type of study? This is a question we hear often from our dissertation assistance  clients, and with good reason. University guidelines for results chapters often contain vague instructions that guide you to include “appropriate tables” without specifying what exactly those are. To help clarify on this point, we asked our qualitative analysis experts to share their recommendations for tables to include in your Chapter 4.

Demographics Tables

As with studies using quantitative methods , presenting an overview of your sample demographics is useful in studies that use qualitative research methods. The standard demographics table in a quantitative study provides aggregate information for what are often large samples. In other words, such tables present totals and percentages for demographic categories within the sample that are relevant to the study (e.g., age, gender, job title). 

If conducting qualitative research  for your dissertation, however, you will use a smaller sample and obtain richer data from each participant than in quantitative studies. To enhance thick description—a dimension of trustworthiness—it will help to present sample demographics in a table that includes information on each participant. Remember that ethical standards of research require that all participant information be deidentified, so use participant identification numbers or pseudonyms for each participant, and do not present any personal information that would allow others to identify the participant (Blignault & Ritchie, 2009). Table 1 provides participant demographics for a hypothetical qualitative research study exploring the perspectives of persons who were formerly homeless regarding their experiences of transitioning into stable housing and obtaining employment.

Participant Demographics

Tables to Illustrate Initial Codes

Most of our dissertation consulting clients who are conducting qualitative research choose a form of thematic analysis . Qualitative analysis to identify themes in the data typically involves a progression from (a) identifying surface-level codes to (b) developing themes by combining codes based on shared similarities. As this process is inherently subjective, it is important that readers be able to evaluate the correspondence between the data and your findings (Anfara et al., 2002). This supports confirmability, another dimension of trustworthiness .

A great way to illustrate the trustworthiness of your qualitative analysis is to create a table that displays quotes from the data that exemplify each of your initial codes. Providing a sample quote for each of your codes can help the reader to assess whether your coding was faithful to the meanings in the data, and it can also help to create clarity about each code’s meaning and bring the voices of your participants into your work (Blignault & Ritchie, 2009).

Table 2 is an example of how you might present information regarding initial codes. Depending on your preference or your dissertation committee’s preference, you might also present percentages of the sample that expressed each code. Another common piece of information to include is which actual participants expressed each code. Note that if your qualitative analysis yields a high volume of codes, it may be appropriate to present the table as an appendix.

Initial Codes

Tables to Present the Groups of Codes That Form Each Theme

As noted previously, most of our dissertation assistance clients use a thematic analysis approach, which involves multiple phases of qualitative analysis  that eventually result in themes that answer the dissertation’s research questions. After initial coding is completed, the analysis process involves (a) examining what different codes have in common and then (b) grouping similar codes together in ways that are meaningful given your research questions. In other words, the common threads that you identify across multiple codes become the theme that holds them all together—and that theme answers one of your research questions.

As with initial coding, grouping codes together into themes involves your own subjective interpretations, even when aided by qualitative analysis software such as NVivo  or MAXQDA. In fact, our dissertation assistance clients are often surprised to learn that qualitative analysis software does not complete the analysis in the same ways that statistical analysis software such as SPSS does. While statistical analysis software completes the computations for you, qualitative analysis software does not have such analysis capabilities. Software such as NVivo provides a set of organizational tools that make the qualitative analysis far more convenient, but the analysis itself is still a very human process (Burnard et al., 2008).

Because of the subjective nature of qualitative analysis, it is important to show the underlying logic behind your thematic analysis in tables—such tables help readers to assess the trustworthiness of your analysis. Table 3 provides an example of how to present the codes that were grouped together to create themes, and you can modify the specifics of the table based on your preferences or your dissertation committee’s requirements. For example, this type of table might be presented to illustrate the codes associated with themes that answer each research question. 

Grouping of Initial Codes to Form Themes

Tables to Illustrate the Themes That Answer Each Research Question

Creating alignment throughout your dissertation is an important objective, and to maintain alignment in your results chapter, the themes you present must clearly answer your research questions. Conducting qualitative analysis is an in-depth process of immersion in the data, and many of our dissertation consulting  clients have shared that it’s easy to lose your direction during the process. So, it is important to stay focused on your research questions during the qualitative analysis and also to show the reader exactly which themes—and subthemes, as applicable—answered each of the research questions.

Below, Table 4 provides an example of how to display the thematic findings of your study in table form. Depending on your dissertation committee’s preference or your own, you might present all research questions and all themes and subthemes in a single table. Or, you might provide separate tables to introduce the themes for each research question as you progress through your presentation of the findings in the chapter.

Emergent Themes and Research Questions

Bonus Tip! Figures to Spice Up Your Results

Although dissertation committees most often wish to see tables such as the above in qualitative results chapters, some also like to see figures that illustrate the data. Qualitative software packages such as NVivo offer many options for visualizing your data, such as mind maps, concept maps, charts, and cluster diagrams. A common choice for this type of figure among our dissertation assistance clients is a tree diagram, which shows the connections between specified words and the words or phrases that participants shared most often in the same context. Another common choice of figure is the word cloud, as depicted in Figure 1. The word cloud simply reflects frequencies of words in the data, which may provide an indication of the importance of related concepts for the participants.

As you move forward with your qualitative analysis and development of your results chapter, we hope that this brief overview of useful tables and figures helps you to decide on an ideal presentation to showcase the trustworthiness your findings. Completing a rigorous qualitative analysis for your dissertation requires many hours of careful interpretation of your data, and your end product should be a rich and detailed results presentation that you can be proud of. Reach out if we can help  in any way, as our dissertation coaches would be thrilled to assist as you move through this exciting stage of your dissertation journey!

Anfara Jr., V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public.  Educational Researcher ,  31 (7), 28-38. https://doi.org/10.3102/0013189X031007028

Blignault, I., & Ritchie, J. (2009). Revealing the wood and the trees: Reporting qualitative research.  Health Promotion Journal of Australia ,  20 (2), 140-145. https://doi.org/10.1071/HE09140

Burnard, P., Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Analysing and presenting qualitative data.  British Dental Journal ,  204 (8), 429-432. https://doi.org/10.1038/sj.bdj.2008.292

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• Knowledge Base

Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

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

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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• Published: 28 August 2024

Facilitators and barriers of midwife-led model of care at public health institutions of dire Dawa city, Eastern Ethiopia, 2022: a qualitative study

• Mickiale Hailu 1 ,
• Aminu Mohammed 1 ,
• Daniel Tadesse 1 ,
• Neil Abdurashid 1 ,
• Legesse Abera 1 ,
• Samrawit Ali 2 ,
• Yesuneh Dejene 2 ,
• Tadesse Weldeamaniel 1 ,
• Meklit Girma 3 ,
• Tekleberhan Hailemariam 1 ,
• Netsanet Melkamu 1 ,
• Tewodros Getnet 1 ,
• Yibekal Manaye 1 ,
• Tariku Derese 1 ,
• Muluken Yigezu 1 ,
• Natnael Dechasa 1 &
• Anteneh Atle 1  

BMC Health Services Research volume  24 , Article number:  998 ( 2024 ) Cite this article

Metrics details

The midwife-led model of care is woman-centered and based on the premise that pregnancy and childbirth are normal life events, and the midwife plays a fundamental role in coordinating care for women and linking with other health care professionals as required. Worldwide, this model of care has made a great contribution to the reduction of maternal and child mortality. For example, the global under-5 mortality rate fell from 42 deaths per 1,000 live births in 2015 to 39 in 2018. The neonatal mortality rate fell from 31 deaths per 1,000 live births in 2000 to 18 deaths per 1,000 in 2018. Even if this model of care has a pivotal role in the reduction of maternal and newborn mortality, in recent years it has faced many challenges.

To explore facilitators and barriers to a midwife-led model of care at a public health institution in Dire Dawa, Eastern Ethiopia, in 2021.

Methodology

: A qualitative approach was conducted at Dire Dawa public health institution from March 1–April 30, 2022. Data was collected using a semi-structured, in-depth interview tool guide, focused group discussions, and key informant interviews. A convenience sampling method was implemented to select study participants, and the data were analyzed thematically using computer-assisted qualitative data analysis software Atlas.ti7. The thematic analysis with an inductive approach goes through six steps: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up.

Two major themes were driven from facilitators of the midwife-led model of care (professional pride and good team spirit), and seven major themes were driven from barriers to the midwife-led model of care (lack of professional development, shortage of resources, unfair risk or hazard payment, limited organizational power of midwives, feeling of demoralization absence of recognition from superiors, lack of work-related security).

The midwifery-led model of care is facing considerable challenges, both pertaining to the management of the healthcare service locally and nationally. A multidisciplinary and collaborative effort is needed to solve those challenges.

Peer Review reports

Introduction

A midwife-led model of care is defined as care where “the midwife is the lead professional in the planning, organization, and delivery of care given to a woman from the initial booking to the postnatal period“ [ 1 ]. Within these models, midwives are, however, in partnership with the woman, the lead professional with responsibility for the assessment of her needs, planning her care, referring her to other professionals as appropriate, and ensuring the provision of maternity services. Most industrialized countries with the lowest mortality and morbidity rates of mothers and infants are those in which midwifery is a valued and integral pillar of the maternity care system [ 2 , 3 , 4 , 5 ].

Over the past 20 years, midwife-led model of care (MLC) has significantly lowered mother and infant mortality across the globe. In 2018, there were 39 deaths for every 1,000 live births worldwide, down from 42 in 2015. From 31 deaths per 1,000 live births in 2000 to 18 deaths per 1,000 in 2018, the neonatal mortality rate (NMR) decreased. The midwifery-led care approach is regarded as the gold standard of care for expectant women in many industrialized nations, including Canada, Australia, the United Kingdom, Sweden, the Netherlands, Norway, and Denmark. Evidence from those nations demonstrates that women and babies who get midwife-led care, as opposed to alternative types of care, experience favorable maternal outcomes, fewer interventions, and lower rates of fetal loss or neonatal death [ 6 , 7 , 8 ].

In Pakistan, the MLC was accompanied by many challenges. Some of the challenges were political threats, a lack of diversity (midwives had no opportunities for collaborating with other midwives outside their institutions), long duty hours and low remuneration, a lack of a career ladder, and a lack of socialization (the health centers are isolated from other parts of the country due to relative geographical inaccessibility, transportation issues, and a lack of infrastructure). Currently, in Pakistan, 276 women die for every 100,000 live births, and the infant mortality rate is 74/1000. But the majority of these deaths are preventable through the midwife-led care model [ 7 ].

The MLC in African countries has faced many challenges. Shortages of resources, work overload, low inter-professional collaboration between health facilities, lack of personal development, lack of a well-functioning referral system, societal challenges, family life troubles, low professional autonomy, and unmanageable workloads are the main challenges [ 8 ].

Due to the aforementioned challenges, Sub Saharan Africa (SSA) is currently experiencing the highest rate of infant mortality (1 in 13) and is responsible for 86% of all maternal fatalities worldwide. As a result, it is imperative to look at the MLC issues in low-income countries, which continue to be responsible for 99% of all maternal and newborn deaths worldwide [ 8 , 9 ].

Ethiopia’s has a Maternal mortality rate (MMR) and NMR of 412 per 100,000 live births and 33 per 1000 live births, respectively, remain high, making Ethiopia one of the largest contributors to the global burden of maternal and newborn deaths, placed 4th and 6th, although MLC could prevent a total of 83% of all neonatal and maternal fatalities in an environment that supports it. The MMR & infant mortality rate (IMR) in the research area were indistinguishable from that, at 150 per 100,000 live births and 67 fatalities per 1,000 live births, respectively [ 10 , 11 , 12 , 13 ].

Since the Federal Ministry of Health is currently viewing midwifery-led care as an essential tool in reducing the maternal mortality ratio and ending preventable deaths of newborns, exploring the facilitators and barriers of MLC may have a great contribution to make in reducing maternal and newborn mortality [ 14 ]. Since there has been no study done in Ethiopia or the study area regarding the facilitators and barriers of MLC, the aim of this research was to explore the facilitators and barriers of MLC in Dire Dawa City public health institutions.

In so doing, the research attempted to address the following research questions:

What were the facilitators for a midwife-led model of care at the Dire Dawa city public health institution?

What were the barriers to a midwife-led model of care at the Dire Dawa city public health institution?

Study setting and design

Institutional based qualitative study was conducted from March 01-April 30, 2022 in Dire Dawa city. Dire Dawa city is one of the federal city administrations in Ethiopia which is located at the distance of 515killo meters away from Addis Ababa (the capital city) to the east. The city administration has 9 urban and 38 rural kebeles (kebeles are the smallest administrative unit in Ethiopia). There are 2 government hospitals, 5 private hospitals, 15 health centers, and 33 health posts. The current metro area population of Dire Dawa city is 426,129.Of which 49.8% of them are males and 50.2% females. The total number of women in reproductive age group (15–49 years) is 52,673 which account 15.4% of the total population. It has hot temperature with a mean of 25 degree centigrade [ 15 ].

Study population and sampling procedure

The source population for this study included all midwives who worked at Dire Dawa City public health facilities as well as key informants from appropriate organizations (the focal person for the Ethiopian Midwives Association and maternal and child health (MCH) team leaders). The study encompassed basically 41 healthcare professionals who worked in Dire Dawa public health institutions in total, and the final sample size was decided based on the saturation of the data or information.

From the total 15 Health centers and 2 Governmental Hospitals found in Dire Dawa city administration, 8 Health centers and 2 Governmental Hospitals were selected by non-probability purposive sampling method. In addition to that a non-probability convenience sampling method was used to select midwives who were working in Dire Dawa city public health institutions and key informants from the relevant organization such as Ethiopian midwives association focal person and MCH team leaders. Midwives who were working for at least six months in the institution were taken as inclusion criteria while those who were working as a free service were excluded from the study.

Data collection tool and procedures

Focus groups, in-depth interviews, and key informant interviews were used in collecting data. A voice recorder, a keynote-keeping, and a semi-structured interview tool were all used to conduct the interviews. Voluntary informed written consent was obtained from the study participant’s before they participated in the study. Then an in-depth interview and focus group discussion were held with midwives chosen from various healthcare organizations. The MCH department heads and the Dire Dawa branch of the Ethiopian Midwife Association served as the key informants. In-depth interview (IDI) and key informant interviews (KII) with participants took place only once and lasted for roughly 50–60 min. In the midwives’ duty room, the interview was held. Six to eight people participated in focus group discussions (FGD), which lasted 90 to 100 min. Two midwives with experience in gathering qualitative data gathered the information.

Data quality control

The qualitative design is prone for bias but open-ended questions were used to avoid acquiescence and 2 day proper training was given for the data collector regarding taking keynotes and recording using a tape recorder. For consistency and possible modification, a pretest was done in one FGD and In-depth interviews at non selected health institutions of Dire Dawa city administrations. A detailed explanation was given for the study participants about the objectives of the study prior to the actual data collections. All (FGDs, key informant interview and In-depth interviews) were taken in a silent place.

Data analysis

Atlas.ti7, a qualitative data analysis program, was used for analyzing the data thematically. An inductive approach to thematic analysis involves six steps: familiarization, coding, generation of themes, review of themes, defining and naming of themes, and writing up. By listening to the taped interview again, the data was transcribed. The participants’ well-spoken verbatim was used to extract and describe the inductive meanings of the statements. The data was then coded after that. Each code describes the concept or emotion made clear in that passage of text. Then we look at the codes we’ve made, search for commonalities, and begin to develop themes. To ensure the data’s accuracy and representation, the generated themes were reviewed. Themes were defined and named, and then the analysis of the data was written up.

Trustworthiness of data

Meeting standards of trustworthiness by addressing credibility, conformability, and transferability ensures the quality of qualitative research. Data triangulation, data collection from various sites and study participants, the use of multiple data collection techniques (IDI, KII, and FGD), multiple peer reviews of the proposal, and the involvement of more than two researchers in the coding, analysis, and interpretation decisions are all instances of the methods that were used in order to fulfill the criteria for credibility. To increase its transferability to various contexts, the study gave details of the context, sample size and sampling method, eligibility criteria, and interview processes. To ensure conformability, the research paths were maintained throughout the study in accordance with the work plan [ 16 , 17 ].

Background characteristics of the study participants

In this study, a total of 41 health care providers who are working in Dire Dawa public health facilities participated in the three FGDs, six KIIs, and fifteen IDIs. The years of experience of study participants range from one year to 12 years. The participants represented a wide age range (30–39 years), and the educational status of the respondents ranged from diploma to master’s degree. (Table  1 )

As shown in Table  2 , from the qualitative analysis of the data, two major themes were driven from facilitators of MLC, and seven major themes were driven from barriers to MLC. (Table  2 ).

Facilitators of midwife-led model of care at a public health institution of Dire Dawa city, Eastern Ethiopia, in 2021

Professional pride.

This study found that saving the lives of mothers and newborns was a strong facilitator. Specifically, it was motivational to have skills within the midwifery domain, such as managing the full continuum of care during pregnancy and labour, supporting women in having normal physiologic births, being able to handle complications, and building relationships with the women and the community, as mentioned below by one of the IDI participants.

“I am so proud since I am a midwife; nothing is more satisfying than seeing a pregnant mother give birth almost without complications. I always see their smile and happiness on their faces , especially in the postpartum period , and they warmly thank me and say , “Here is your child; he or she is yours.” They bless me a lot. Even sometimes , when they sew me in the transport area , cafeteria , or other area , they thank me warmly , and some of them also want to invite me to something else. The sum total of those things motivates me to be in this profession or to provide midwifery care.“ IDI participants.

This finding is also supported by other participants in FGD.

“We have learned and promised to work as midwives. We are proud of our profession , to help women and children’s health. The greatest motivation is that we are midwives , we love the profession , and we are contributing a great role in decreasing maternal and child mortality….” FGD discussant.

Good teamwork

The research revealed that good midwifery teamwork and good social interaction within the staff have become facilitators of MLC. FGD participants share their experiences of working in a team.

“In our facility , all the midwives have good teamwork; we have good communication , and we share client information accurately and timely. In case a severe complication happens , we manage it as a team , and we try to cover the gap if some of our staff are absent. Further from that , we do have good social interactions in the case of weeding , funeral ceremonies , and other social activities. We do have good team spirit; we work as a team in the clinical area , and we also have good social relationships. “If some of our staff gets sick or if she or he has other social issues , the other free staff will cover her or his task.” FGD discussant.

Another participant from IDI also shared the same experience regarding their good teamwork and their social interactions.

“As a maternal and child health team , we do have a good team spirit , not only with midwives but also with other professions. We are not restricted by the ward that we assign. If there is a caseload in any unit , some midwives will volunteer to help the other team. Most of the time in the night , we admit more than 3 or 4 labouring mothers at the same time. Since in our health center only one midwife is assigned in the night , we always call nurses to help us. This is our routine experience.” IDI participants.

Barriers of midwife-led model of care at a public health institution of Dire Dawa city, Eastern Ethiopia, in 2021

Lack of professional development.

This study revealed that insufficient opportunities for further education and updated training were the main barriers for MLC. Even the few trainings and update courses that were actually arranged were unavailable to them, either because they did not meet the criteria seated or because the people who work in administration were selected. Even though opportunities are not arranged for them to upgrade themselves through self-sponsored. One of the participants from IDI narrates her opinion about opportunities for further education as follows:

“Training and updates are not sufficient; currently we are almost working with almost old science. For example , the new obstetrics management protocol for 2021 has been released from the ministry of health , and many things have changed there. But we did not receive any training or even announcements. Even the few trainings and update courses that were truly organized and turned in to us are unavailable since the selection criteria are not fair. As a result , we miss those trainings either because we did not meet the selection criteria or because those who work in administration are prioritized.” IDI participant.

FGD discussants also support this idea. She mentioned that even though opportunities are not arranged for them to upgrade themselves through self-sponsorship,

“There is almost no educational opportunity in our institution. Every year , one or two midwives may get institutional sponsorship. Midwives that will be selected for this opportunity are those who have served for more than five to ten years. Imagine that to get this chance , every midwife is expected to serve five or more years. Not only this , even if staff want to learn or upgrade at governmental or private colleges through self-sponsored programmes , whether at night or in an extension programme , they are not cooperative. Let me share with you my personal experience. Before two years , I personally started my MSc degree at Dire Dawa University in a weekend programme , and I have repeatedly asked the management bodies to let me free on weekends and to compensate me at night or any time from Monday to Friday. Since they refuse to accept my concern , I withdraw from the programme.“ FGD discussant.

Shortage of resource

The finding indicates that a shortage of equipment, staff, and rooms or wards was a challenge for MLC. Midwives claimed they were working with few staff, insufficient essential supplies, and advanced materials. This lack of equipment endangers both the midwives and their patients. One of the participants from IDI narrates her opinion about the shortage of resources as follows:

“Of course there is a shortage of resources in our hospital , like gloves and personal protective devices. Even the few types of medical equipment available , like the autoclave , forceps , vacuum delivery couch , and BP apparatus , are outdated , and some of them are unfunctional. If you see the Bp apparatus we used in ANC , it is digital but full of false positives. When I worked in the ANC , I did not trust it and always brought the analogue one from other wards. This is the routine experience of every staff member.“ IDI participants.

Another participant from IDI also shared the same experience regarding the crowdedness of rooms or wards.

“In our health center , there are no adequate wards or rooms. For example , the delivery ward and postnatal ward are almost in one room. Postnatal mothers and neonates did not get enough rest and sleep because of the sound of laboring mothers. Not only is this , but even the antenatal care and midwifery duty rooms are also very narrow.“ IDI participants.

The study also revealed midwifery staff were pressured to work long hours because they were understaffed, which in turn affected the quality of midwifery care. The experience of a certain midwife is shared as follows:

“I did not think that the management bodies understood the risk and stress that we midwives face. They did not want to consider the risk of midwives even equal to that of other disciplines but lower than the others. For example , in our health centre , during the night , only one midwife is assigned for the next 12 hours , but if you see in the nurse department , two or more nurses are assigned at night in the emergency ward.” IDI participants.

The discussion affirms the fact that being understaffed and not having an adequate allocation of midwife professionals on night shifts are affecting labouring mothers’ ability to get sufficient health midwifery care. The above narration is also supported by the FGD discussant.

“In our case , only one midwife is assigned to the labour ward during the night shift. I think this is the main challenge for midwives that needs attention. Let me share with you my experience that happened months before. While I was on night shift , two labouring mothers were fully dilated within three or four minutes. It was very difficult for me , to manage two labouring mothers at the same time. Immediately , I call one of my nurse friends from the emergency department to help me. If my friend was so busy , what could happen to the labouring mother and also to me? This is not only my experience but also the routine experience of other midwives.” FGD discussant.

Unfair risk or hazard payments

It is reported that the compensation amount paid for risk is lower than in other health professions. The health risks are not any less, but the remuneration system failed to capture the need to fairly compensate midwifery professionals. The narration from the FGD discussant regarding unfair payment is mentioned below.

“Only 470 ETB is paid for midwives as risk payments , which is incomparable with the risks that midwives are facing. But contrary to that , the risk payments for nurses (in emergencies) are about 1200 Ethiopian birr (ETB) , and Anesthesia is 1000 ETB. I did not want to compare my profession with other disciplines , but with the lowest cost , how the risk of midwifery cannot be equal to that of nursing and other professions. I did not know whose professionals made such types of unfair decisions and with what scientific background or base this calculation was done . ” FGD discussant.

The above finding is also supported by an IDI participant.

“………………………….Even though the midwifery profession is full of risks , with the current Ethiopian health care system , midwives are being paid the lowest risk payments compared to other disciplines…………….” IDI participants.

Limited organizational power of midwives

Midwives’ interviews reported that limited senior midwifery positions in the health system have become the challenge of midwifery care. This constrains the decision-making power and capability of midwives. This was compounded by limited opportunities for midwifery personnel to address their concerns to the responsible bodies, as stated by one of the key informants.

“Our staff has many concerns , especially professional-related concerns , which can contribute to the quality of midwifery care. Personally , as department head , I have tried to address those concerns in different management meetings at different times. But since the leadership positions are dominated by other disciplines , many of our staff concerns have not been solved yet. But let me tell you my personal prediction… If those concerns are not solved early and if this trend continues , the quality of midwifery care will be in danger.“ Participant from Key Informant.

The above finding is also supported by another IDI participant.

“In our hospital , at every hierarchal and structural level , midwives are not well represented. That is why all of our challenges or concerns have not been solved yet. For example , as a structure in the Dire Dawa Health Office (DDHO) , there is a team of management related to maternal and child health. But unfortunately , those professionals working there are not midwives. I was one of three midwives chosen to meet with Dr. X (former DDHO leader) to discuss this issue. At the time , we were reaching an agreement that two or three midwives would be represented on that team. But since a few months later the leader resigned , the issue has not gotten a solution yet.“ IDI participant.

Feeling of demoralization

One of the main concerns reported by the participants during the interviews was a feeling of demoralization induced by both their clients and their supervisors about barriers to midwifery care. They reported having been verbally abused by their patients, something that made them feel that their hard work was being undermined, as stated by an FGD participant.

“I don’t think there is any midwife who would be happy for anybody to lose their baby , or that there is any midwife who would want a woman to die. These things are accidents , but the patient and leaders will always blame the midwife.” FDG discussant.

A narration from an IDI participant also mentioned the following:

“……….If something happens , like a conflict with the patients or clients , the management is on the patient side. Not only that , the way in which they communicate with us is in an aggressive or disrespectful manner . ” IDI participant.

Absence of recognition or /motivation from superiors

This study revealed that midwives experience a loss of motivation at work due to limited support from their superiors. Their effort is used only for reporting purposes. A midwife from FGD shared her experience as follows.

“In our scenario , till the nearest time , the maternal and child health services are provided in a good way. But this was not easy; it is the cumulative effort of midwives. But unfortunately , only those in managerial positions are recognized. Nothing was done for us despite our efforts. To me , our efforts are used only for reporting purposes.” FGD discussant.

This finding was also supported by IDI participants.

“Even though we have good achievements in the MCH services , there is no motivation mechanism done to motivate midwives.” But if something or a minor mistake happens , they are on the front lines to intimidate us or write a warning letter. Generally , their concern is a report or a number issue. We are tired of such types of scenarios.” IDI participant.

Insufficient of work-related security

One of the main concerns reported by the participants during the interviews was the work related security, which has become a challenge for MLC. The midwives’ work environment was surrounded by insecurity, especially during night shifts, when midwives were facing verbal and even physical attack, as mentioned by participants.

“In the labour ward , especially at night , we face many security-related issues. The families of labouring mothers , especially those who are young , are very aggressive. Sometimes they even want to enter the delivery room. They did not hear what we told them to do , but if they hear any labour sounds from their family , they disturb the whole ward. This leads to verbal abuse , and sometimes we face physical abuse. There may be one or two security personnel at the main gate , but since the delivery ward is far from the main gate , they do not know what is happening in the delivery ward. When things become beyond our scope , we call security guards. Immediately after the security guards go back , similar things will continue. What makes it difficult to manage such situations is that only one midwife is assigned at night , and labouring mothers will not get quality midwifery care.” IDI participant.

FGD discussants also shared their experience that their working environment is full of insecurity.

“In case any complications occur , especially at night , it is very difficult to tell the labouring mother’s family or husband unless we call security personnel. It is not only swearing that we face but also that they intimidate us.” FDG discussant.

Discussions

The aim of this study was to explore facilitators’ and barriers to a midwifery-led model of care at Dire Dawa public health facilities. In this study, professional pride was the main facilitator of the midwifery-led model of care. Another qualitative study that examined the midwifery care challenges and factors that motivate them to remain in their workplace lends confirmation to this conclusion. It was found that a strong feeling of love for their work was the main facilitator’s midwifery-led model of care [ 9 ]. Having a good team spirit was also another facilitator’s midwifery-led model of care in our study. Another study’s findings confirmed this one, which emphasizes that building relationships with the midwives, women, and community was the driving force behind providing midwifery care [ 7 , 18 ].

The midwives in this study expressed a need for additional professional training, updates, and competence as part of their continuing professional development. Similar findings have been reported in the worldwide literature that midwives were struggling for survival due to a lack of limited in-service training opportunities to improve their knowledge and skills [ 19 ]. This phenomenon does not seem to differ between settings in high-, middle-, and low-income countries [ 7 , 9 , 18 ], in which midwives experienced difficult work situations due to a lack of professional development to autonomously manage work tasks, which made them feel frustrated, guilty, and inadequate. As such, this can contribute to distress and burnout, which in turn prevent midwives from being able to provide quality care and can eventually cause them to leave the profession [ 19 ].

Shortages of resources (shortage of staff, lack of physical space, and equipment) were the other reported barriers to midwifery care explored in this study. They reported that they are working in an environment with a shortage of resources, which leads to poor patient outcomes. This finding is supported by many other studies conducted around the globe [ 20 , 21 , 22 , 23 ]. Another qualitative finding, which likewise supports the aforementioned finding, which emphasizes that a shortage of resources was reported as a barrier to providing adequate midwifery care [ 19 ]. Delivery attended by skilled personnel with appropriate supplies and equipment has been found to be strongly associated with a reduction in child and maternal mortality [ 24 ].

The feeling of demoralization and lack of motivation from their superiors were other barriers to midwifery care explored in this study. This finding is concurrent with other studies conducted around the globe [ 19 , 25 , 26 , 28 ]. The above finding is also is in accord with another qualitative narration, which emphasizes that feelings of demoralization and a lack of motivation were the main challenges of midwifery care [ 22 ]. Positive support from supervisors has been demonstrated to be important for the quality of services that health workers are able to deliver. In the World Health Organization’s report on improving performance in healthcare, the WHO stresses that supportive supervision can contribute to the improved performance of health workers [ 27 ].

Unfair risk payment was the other challenge identified by the current study. Even though there is no difference in the risk they face among health professionals, the risk payment for midwives is very low compared to others. This finding was in conformity with another qualitative narration, which emphasizes that the lack of an equitable remuneration system was experienced by the DRC midwives, and it has also been confirmed to be highly problematic in other studies in low- and middle-income settings [ 7 , 8 , 22 , 28 ], leading to serious challenges. In settings where salaries are extremely low or unpredictable, proper remuneration is seen as crucial to worker motivation and the quality of midwifery care [ 29 , 30 ].

The limited organizational power of midwives was another identified challenge of MLC. This finding was in step with other studies that emphasize that limited senior midwifery positions in the health system constrain the decision-making power and capability of midwives. This was compounded by limited opportunities for midwifery personnel to address their concerns to the responsible bodies. Hence, midwives need to take control of their own situations. When midwives are included in customizing their work environments, it has proven to result in improved quality of care for women and newborns around the globe [ 8 , 15 ].

Lack of work-related security was another barrier to MLC explored in this study, in which the midwives’ work environment was surrounded by insecurity, especially during night shifts, when midwives are facing verbal and even physical attack, as mentioned by participants. This finding is supported by many other studies conducted around the globe [ 22 , 23 , 25 , 31 ]. The above finding is also in agreement with another qualitative narration, which emphasizes that the midwives’ work environment was surrounded by insecurity, especially during night shifts due to a lack of available security personnel; they often felt frightened on their way to and from work [ 7 ]. In order for midwives to provide quality care, it is crucial to create supportive work environments by ensuring sufficient pre-conditions, primarily security issues [ 31 ].

Conclusions

The study findings contribute to a better understanding of the facilitators’ and barriers of a midwifery-led model of care in the case of Dire Dawa public health facilities. Professional pride and having good team spirit were the main facilitators of midwifery-led model care. Contrary to that, insufficient professional development, shortage of resources, feeling of demoralization, lack of motivation, limited organizational power of midwives, unfair risk payment, and lack of work-related security were the main barriers to a midwifery-led model of care in the case of Dire Dawa public health facilities. Generally, midwifery care is facing considerable challenges, both pertaining to the management of the healthcare service locally and nationally.

Study implications

The findings of the study have implications for midwifery care practices in Eastern Ethiopia. Addressing these areas could potentially contribute to the reduction of IMR and MMR.

Strengths and limitations

The first strength of the study is that the participants represented different healthcare facilities, both urban and rural, thereby offering deeper and more varied experiences and reflections. A second strength is using a midwife as a moderator. She or he understood the midwives’ situation, thereby making the participants feel more comfortable and willing to share their stories. However, focusing solely on the perspective of the midwives is a limitation.

Recommendations

To overcome the barriers of midwifery care, based on the result of this study and in accordance with the 2020 Triad Statement made by the International Council of Nurses, the International Confederation of Midwives, and the World Health Organization, it is suggested that policymakers, Ethiopian federal ministry of health, Dire dawa health office, and regulators in Dire Dawa city and settings with similar conditions coordinate actions in the following:

To the Ethiopian federal ministry of health (FMOH)

Should strengthen regular and continuous educational opportunities, trainings, and updates for midwives, prioritizing and enforcing policies to include adequate and reasonable remuneration and hazard payment for midwives. Support midwifery leadership at all levels of the health system to contribute to health policy development and decision-making.

To dire Dawa health Bureau

Ensure decent working conditions and an enabling environment for midwives. This includes reasonable working hours, occupational safety, safe staffing levels, and merit-based opportunities for career progression. Special efforts must be made to ensure safe, respectful, and enabling workplaces for midwives operating on the night shift. Midwifery leaders should be involved in management bodies within an appropriate legal framework. Made regular mentorships on the functionality of different diagnostic instruments in respective health facilities.

To Dire Dawa public health facility’s

Create an arena for dialogue and implement a more supportive leadership style at the respective health facilities. Should address professional-related concerns of midwives early. Ensure midwives’ representation at the management bodies. Ensure the selection criteria for educational opportunities and different trainings are fair and inclusive. Ensure the safety and security of midwives, especially those who work night shifts. Should assign adequate staff (midwives and security guards) to the night shifts.

Ethiopian midwifery association

Should influence different stakeholders to solve midwife’s concerns like hazards payment and educational opportunity.

Data availability

All the datasets for this study are available from the corresponding author upon request.

Abbreviations

Focused group discussion

In-depth interview

Infant mortality rate

Key informant interview

Maternal and child health

Midwives led model of care

Neonatal mortality rate

The midwives model of care. Midwives alliance North America, the MANA core documents, 2020.

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Acknowledgements

We are very grateful to Dire Dawa University for the financial support for this study and to the College of Medicine and Health for its monitoring ship. All study participants for their willingness to respond to our questionnaire.

this work has been funded by Dire Dawa University for data collection purposes. The Dire Dawa University College of Medicine and Health Sciences was involved in the project through monitoring and evaluation of the work from the beginning to the result submission. However, this organization was not involved in the design, analysis, critical review of its intellectual content, or manuscript preparation, and its budget did not include publication.

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MH developed the study proposal, served as the primary lead for study implementation and data analysis/interpretation, and was a major contributor in writing and revising all drafts of the paper. AM, DT, NA, LA, and SA supported study implementation and data analysis, and contributed to writing the initial draft of the paper. YD, TW, MG, TH and, NM supported study recruitment and contributed to writing the final draft of the paper. TG, YM, TD, MY, ND and, AA conceptualized, acquired funding, and led protocol development for the study, co-led study implementation and data analysis/interpretation, and was a major contributor in writing and revising all drafts of the paper. All authors contributed to its content. All authors read and approved the final manuscript.

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All methods were followed in accordance with relevant guidelines and regulations. The institutional review board of Dire Dawa University has also examined and evaluated it for its methodological approach and ethical concerns. Ethical clearance was obtained from Dire Dawa University Institutional Review Board and an official letter from research affairs directorate office of Dire Dawa University was submitted to Dire Dawa health office and it was distributed to selected health institutions. Voluntary informed written consent was obtained from the study participant’s right after the objectives of the study were explained to the study participants and confidentiality of the study participants was assured throughout the study period. Participants were informed that they have the right to terminate the discussion (interview) or they can’t answer any questions they didn’t want to answer.

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Hailu, M., Mohammed, A., Tadesse, D. et al. Facilitators and barriers of midwife-led model of care at public health institutions of dire Dawa city, Eastern Ethiopia, 2022: a qualitative study. BMC Health Serv Res 24 , 998 (2024). https://doi.org/10.1186/s12913-024-11417-x

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Exploring the experiences of English-speaking women who have moved to Israel and subsequently used Israeli fertility treatment services: A qualitative study

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

* E-mail: [email protected]

Affiliation Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom

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

Affiliation Bob Shapell School of Social Work, Tel Aviv University, Tel Aviv, Israel

Roles Formal analysis

Affiliation Health Services Management Centre, University of Birmingham, Edgbaston, Birmingham, United Kingdom

• Lucy Davies, 
• Gilles de Wildt, 
• Yael Benyamini, 
• Anoushka Ramkumar, 
• Rachel Adams

• Published: August 28, 2024
• https://doi.org/10.1371/journal.pone.0309265
• Reader Comments

Israel’s pronatalist cultures result in a social expectation to have children and drive Israel’s fertility rate of 2.9. Israeli policy reflects this through funding unlimited fertility treatment up to two children. Societal pressure to have children exacerbates challenges of fertility treatment. Furthermore, the lack of financial burden creates a culture of perseverance following treatment failures. Whilst the experiences of Israeli women using fertility treatment have been studied, the experiences of women who migrated to Israel and were therefore raised in a different society have not. This study aimed to address this gap in knowledge.

A qualitative study using semi-structured interviews to investigate the experiences of 13 English-speaking women who utilised Israeli state funded fertility treatment. Participants were located across Israel and were recruited using purposive sampling through social media. Data was analysed using framework analysis.

Despite not being aimed at specific ethnic or religious groups, all respondents were Jewish. Three themes were identified: 1 . Systemic factors : The lack of financial burden was positive, however, participants struggled to navigate the bureaucratic healthcare system, especially when experiencing a language barrier. 2 . Influence of others : Encountering a cold bedside manner alongside contending with the expectations of a pronatalist society was challenging. Participants utilised support from other migrants who appreciated the same culture shock. Understanding of healthcare professionals regarding shared religious values further improved treatment experiences. 3 . Impact of journey : Participants often withdrew socially and the treatment process implicated upon their lives, jobs and relationships.

Navigating a bureaucratic system and pronatalist society are difficulties associated with fertility treatment in Israel. The lack of financial burden and an understanding of religious and cultural beliefs by healthcare providers improved treatment experience. Better provision of resources in English and further research into supporting women who are navigating Israel’s pronatalist society is required.

Citation: Davies L, de Wildt G, Benyamini Y, Ramkumar A, Adams R (2024) Exploring the experiences of English-speaking women who have moved to Israel and subsequently used Israeli fertility treatment services: A qualitative study. PLoS ONE 19(8): e0309265. https://doi.org/10.1371/journal.pone.0309265

Editor: Syed Khurram Azmat, Marie Stopes International, PAKISTAN

Received: August 31, 2023; Accepted: August 7, 2024; Published: August 28, 2024

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

Data Availability: Data cannot be made publicly available due to ethical restrictions. Data sharing requests may be sent to institutional representative Dr. Connie Wiskin ( [email protected] ) subject to a data sharing agreement.

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

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

Introduction

Israel is widely recognised as having a pronatalist society [ 1 ]. Pronatalist societies are associated with a strong family orientation and an expectation to have children [ 2 ]. Motherhood is regarded as an essential aspect of life and voluntary childlessness is rare and, for many, socially unacceptable [ 3 , 4 ]. This is reflected in the finding that 80% of Israelis believe child-rearing is the “greatest joy of life” [ 5 ]. Explanatory factors for the desire to reproduce include the biblical commandment to those following Abrahamic religions to ‘be fruitful and multiply’ (Genesis 1:28), the traumas of the Holocaust, and the aspiration to maintain the Jewish population in Israel [ 1 , 6 ].

Pronatalist cultures are reflected in Israel’s total fertility rate of 2.9 [ 7 ]. This is much higher than the average fertility rate in 2021 of 1.58 within the Organisation for Economic Co-operation and Development (OECD) countries [ 8 ]. According to The World Bank data, Israel’s total fertility rate has been stable for at least 30 years [ 7 ]. Additionally, a 2021 paper describes the fertility rate in Israel as roughly double the rate observed in other economically similar countries [ 9 ]. Data from Israel’s Central Bureau of Statistics 2021 report shows that within Israel Jewish women have the highest fertility rate of 3.13 compared with 3.01 for Muslim women, 1.77 for Christian women and 2.00 for Druze women [ 10 ]. All Jewish sub-groups are seeing a rise in fertility rate, whereas rates for Muslim, Christian and Druze women have decreased [ 9 ].

According to Israel’s Central Bureau of Statistics, its population in December 2022 was estimated at 9,656,000 residents, 73.6% of whom are Jews, 21.1.% Arabs and 5.3% others, including Druze. Throughout 2022 the Israeli population increased by 2.2%, with 38% of this due to migration balance, and the remainder due to natural growth. Overall, 178,000 infants were born in 2022, 74.8% to Jewish mothers, 23.8% to Arab mothers and 1.4% to mothers of Others [ 11 ].

Health policy in Israel reflects the social expectation to have children through provision of state funded fertility treatment [ 12 ]. All Israeli residents are entitled to healthcare through a National Health Insurance system with compulsory insurance based on a choice of one out of four competing health plans. Workers pay a set health tax as a percentage of their wage towards funding the healthcare system. Although the insurance is comprehensive, covering most emergency, primary and inpatient care, further co-payments for certain services or medications may be required [ 13 ]. However, even the basic insurance entitles women to receive unlimited cycles of state funded fertility treatment, including in vitro fertilisation (IVF), until the birth of up to two live children with their current partner. This policy applies regardless of existing children with other partners, marital status or sexual orientation. Women are eligible to receive IVF up to age 45 years old when using their own gametes or up to age 54 in the case of egg donation [ 14 ]. This policy facilitates Israel having the highest rate of infertility treatment cycles and number of fertility clinics per capita worldwide [ 15 , 16 ].

Whilst the policy of state funded fertility treatment allows individuals who otherwise would not be able to have children to become parents, it is important to note the potential burden imposed by fertility treatment, which is exacerbated within the context of pronatalist societies. Universally infertility has been found to be a ‘major life crisis’ associated with feelings such as guilt and helplessness [ 4 , 17 ]. The high value placed on motherhood by pronatalist states such as Israel increases the vulnerability of fertility patients to psychological distress [ 5 ]. Furthermore, considering voluntary childlessness in Israel is virtually unheard of, this puts additional external pressure on women to conceive [ 14 ]. This pressure may be exacerbated for religious women since a religious lifestyle is typically child centred, therefore religious childless women often find socialising within their community difficult [ 18 ].

Alongside the emotional and psychological burden of the fertility treatment journey there are also physical challenges including breast tenderness, pain at injection sites and risk of ovarian hyperstimulation syndrome [ 19 , 20 ]. There have been concerns regarding long-term health implications of fertility treatment, particularly the increased risk of certain cancers. However, a 2017 review into the association between fertility medication and cancer risk was reassuring, finding while infertility is a risk factor for breast, endometrial and ovarian cancer, there is insufficient evidence to suggest an association between fertility medication and cancer risk [ 21 ].

The social context in Israel has led to a culture of perseverance with fertility treatment [ 22 ]. Although women receive information about the odds of treatment success they are often unrealistically optimistic. While this contributes to their wellbeing it also makes treatment discontinuation difficult [ 14 ]. Women in Israel are therefore likely to endure treatment for a prolonged period, with a study finding 86% of respondents would undergo as many IVF cycles as needed [ 20 ], with some women using as many as 20 treatment cycles [ 14 ].

Current literature has addressed the assisted reproductive technology landscape in Israel. Despite there being a sizeable quantity of literature, it is focused on a population of native Israeli women. Jews are encouraged to migrate to Israel and under the Law of Return all Jews have the right to do so and to receive citizen status immediately [ 23 , 24 ]. Migrants to Israel are unlikely to have grown up in such a highly pronatalist society. Additionally, their native language is unlikely to be Hebrew and they may not be used to the concept of state funded fertility treatment. Demographic data shows that migrants to Israel from lower fertility countries go on to increase their fertility rate [ 9 ]. Considering in 2023 there were approximately 45 thousand migrants who relocated to Israel [ 25 ], it is important to address this gap in the literature surrounding the experiences of non-native women undergoing fertility treatment. Therefore, this study aimed to investigate the experiences of English-speaking women who migrated to Israel and subsequently used state funded fertility treatment.

Study design

A qualitative methodology, using semi-structured interviews, was selected as it allowed flexibility for in-depth exploration of ideas raised by participants. The use of a topic guide provided structure for the interviewer [ 26 ]. This study conformed to Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines [ 27 ].

Study setting

The study was set in Israel, with the lead researcher based in Tel Aviv for the duration of the study and participants located across Israel. Interviews were conducted via Zoom as this was deemed most appropriate given the COVID-19 pandemic.

Participants were native English-speaking women who migrated to Israel after age 18 years and had subsequently used at least one full cycle of largely state funded fertility treatment in Israel. Participants were entitled to benefits according to Israeli National Health Insurance thereby allowing them to receive state funded fertility treatment. Women of any marital status, any treatment outcome and with any number of existing children were included.

Sampling and recruitment

This study utilised purposive sampling, with supplementary snowball sampling in order to improve feasibility of recruitment. Participants were recruited through an advertisement posted in relevant Facebook groups. They included Israel specific parent support groups and fertility support groups. Interested individuals were emailed the participant information sheet and given the opportunity to ask questions. Following receipt of a signed consent form, including consent to participate and consent for recording of the interview, an interview was arranged.

We planned to conduct up to 20 interviews, with recruitment terminated once data saturation was reached. Information was supplied to 19 potential recruits of whom 13 were interviewed. Reasons for non-participation included not meeting the study criteria and being unable to find a suitable time for interview.

Data collection

Participants were interviewed once, in English, by the lead researcher. Interviews were recorded. Prior to interview commencement, the participant demographic form was completed verbally. This included details regarding previous fertility treatment usage.

An iterative topic guide ( S1 File ) was used to structure the interviews. Using open questions allowed the flow of each interview to be participant led and also facilitated in-depth discussion of all ideas raised [ 28 ]. Ultimately the interviews lasted a mean length of 68 minutes (range 46–95 minutes). Immediately after each interview reflective notes were made regarding new topics raised, any issues identified with the topic guide and whether data saturation was being approached, and reviewed with the research team (RA, GdW, YB).

Following interviewing 13 women, no new themes were being identified, the team therefore agreed that data saturation had been reached. A larger sample size may have highlighted further findings, however, since no new themes were raised, and with time as limiting factor data, collection was concluded.

Data analysis

Interviews were transcribed intelligent verbatim. Utilisation of the constant comparison method allowed thorough investigation of themes [ 29 ].

The Framework analysis approach was used to guide analysis [ 30 ]. The clear steps provided by Ritchie and Spencer’s five step approach guided the lead researcher throughout analysis [ 30 ]. NVivo 12 software was used for management of data [ 31 ]. The lead researcher became familiar with data during transcription and reading of the transcripts. Coding of three interviews and then developing codes into thematic categories allowed identification of a framework. This framework was then applied to the remaining transcripts. Indexed data was charted and summarised using Excel. Finally, themes were formulated from data through an inductive approach and interpretations were made. Multiple transcripts were coded by a secondary researcher and themes were discussed within the research team. The primary researcher is a British, medical student who has existing experience with fertility treatment in the United States. Reflexivity was maintained throughout with the researcher remaining aware of how her background and opinions may have influenced the study and its findings [ 32 ].

Ethical approval for this study was received from the University of Birmingham’s BMedSci Internal Research Ethics Committee (IREC2020) and the Tel Aviv University Institutional Review Board (0003890–2). Data cannot be shared publicly because confidentiality was promised, and the level of detail within the transcripts may make interviewees identifiable despite attempts to anonymise them. Data are available from the corresponding author subject to completion of a data sharing agreement.

Inclusivity in global research

Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in the Supporting Information ( S1 Checklist )

13 women were interviewed throughout February 2022. Participants had a mean age of 36 years (range 25–50 years) and prior to commencing fertility treatment had conceived between 0 and 3 children through unassisted conception. At the time of interview all participants had either had a live birth or were pregnant through fertility treatment. All participants were Jewish, despite recruitment efforts not being targeted at one specific religious group. Advertisements were posted in various Israel specific Facebook groups, for example, city specific, parenting support and fertility support groups (no groups with religious affiliations for the target population were found). All women had at least a basic understanding of Hebrew and were from North America or the UK. Participant characteristics are displayed in Table 1 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0309265.t001

Three key themes were developed: systemic factors, influence of others and impact of journey. These themes, alongside their subthemes, are displayed in Fig 1 . Socio-cultural factors were a central concept connecting each theme.

The three key themes and their subthemes are displayed, with the underlying concept of the impact of socio-cultural factors shown in the centre.

https://doi.org/10.1371/journal.pone.0309265.g001

1. Systemic factors

Financial implications..

Participants “ didn’t feel a financial burden” (P1) when commencing treatment or the need to limit the number of cycles they used due to affordability. Participants felt “grateful” (P13) to have received fertility treatment in Israel since finances did not determine family size.

“ … if I was living in a different country, I probably wouldn’t be able to afford [fertility treatment], let alone as many cycles as I ended up needing to conceive” (P1)

Due to co-payments fertility treatment did still incur a minor cost, however, this was like “pennies” (P1) compared with other countries. Without a major financial burden, participants were able to focus on their “ actual emotions” (P1), rather than what else could be done with the money.

“ … each failed round I wasn’t like, oh my God that’s money for my house, oh that’s my mortgage” (P9)

Some grievances with the provision of fertility treatment were accounted to the treatment being state funded. These grievances included a perception of acceptance towards failed cycles since the patient was not paying for it and a worse attitude to patient care. The low cost of treatment made these frustrations with the Israeli healthcare system bearable.

“ Well, it would have been completely unacceptable if I was paying for this. The only reason I’ve lasted so long here is because it costs virtually nothing” (P7)

Navigating the system.

Many participants found the system “bureaucratic” (P12) struggling with the excessive “responsibility on the patient” (P7) and knowing “ what each doctor wanted” (P10).

“ I have to call the hospital for this type of test, you have to call Maccabi [a healthcare service] for this type of test … it’s very decentralised. It can be hard to know what you need, and who to go to for what.” P7

Participants also struggled with the time demands of the treatment process, often waiting “ for hours” (P10) to be seen and spending days waiting for phone calls.

“ You’d get a blood test and you would wait for the phone to ring … God forbid you missed the phone call, and you had to try to call the office back, and it would not work” P5

Participants were also displeased with the overall lack of “continuity” (P12) whereby their management plan could be changed by any of the health centre’s doctors as opposed to one designated doctor.

“… any doctor can look at [your file] and any doctor can update what you should be doing … which was also very frustrating and very worrisome for me” P1

Although participants found that most doctors spoke English, other healthcare professionals or administrative staff often did not. The language barrier was “ very nerve racking” (P1). There was also a lack of resources in English with more information available in Russian and Arabic.

“ … even though I have decent Hebrew … it was definitely an added element of anxiety to walk into like a medical setting, which is really important to understand… I had to like limit myself to what I could actually express” P12

Participants felt part of a “ mass machine” (P9), which was “ very systematic , for better and for worse” (P13). Overall, a “ desire for improvements” (P13) is needed, for example through employing “ built in patient advocates for olim [immigrants to Israel] ” (P12) and improving access to English resources.

2. Influence of others

Healthcare professional attitude..

Participants experienced a “different bedside manner expectation” (P12) in Israel than in their native country. The “culture shock” (P8) of dealing with doctors who were “ so cold , and so not caring” (P8) led to participants feeling “uncomfortable” (P1).

“ … do you choose somebody with bedside manner who is not necessarily the best doctor, or somebody who you know is a superstar, but they’re going to make you feel awful” P13

Since “Anglos really like to have their hands held” (P13), participants felt the attitude whereby healthcare professionals were unwilling to spend time answering questions or provide in-depth explanations of the woman’s circumstance was “harmful … and frustrating” (P13).

“ … they are just rude, impatient, they don’t explain things” P2

Insufficient emotional sensitivity within the fertility system and a “lack of mental health awareness ” (P8) was experienced. Despite this, some participants felt well supported in a logistical sense.

“ Israeli society is not into emotional support, they’re very logistics focussed, they will emotionally support you by helping you practically” P5

Further comfort came from having a healthcare team who understood the participants both religiously and culturally.

“ … my doctor was completely secular, but just before he did the transfer, he said a blessing, in Hebrew … that was such a powerful experience, and I knew I wouldn’t get that anywhere else, like in the world” P5

Through understanding Jewish rules the healthcare team offered guidance regarding the religious elements of fertility treatment.

“ … after my egg retrieval the nurse said to me ‘you’re going to bleed, but you’re not in niddah [Jewish practice whereby a woman is not permitted to touch her husband during her menstrual period nor for seven days afterwards] because it’s a clear scrape’” P12

Cultural expectations.

Israeli society was described as “family focussed” (P5) and participants explained that within religious communities “ it’s a cultural expectation” (P5) to have children early in marriage.

“ … within our circles, obviously sort of religious people… you kind of feel the pressure if you’re not pregnant” P11

Whilst some participants felt the heightened pressure in Israel was due to increased religiosity compared to their home country, the pressure to have children also extended into secular Israeli communities.

“ I don’t think I’d have four kids living in the UK … I think that a lot of people [in Israel] tend to have more kids … even if you’re not religious” P3

Israel has “ much more of an open culture” (P9) than the participants’ native countries. Whilst on the one hand this resulted in experiences of intrusive comments regarding family planning that made it “ very difficult to be in social situations” (P5), it also meant that the expectation to have children allowed the participants’ families and friends to act more sensitively and anticipate a fertility problem and participants felt understood when accessing fertility treatment at a young age. Despite this, not everybody had “ emotional intelligence” (P13) and participants identified a need for “more awareness” (P10) amongst the general public about questioning family circumstance.

Support and assistance.

Fertility treatment is a “difficult experience” (P5) and participants used infertility support groups for emotional support as well as practical advice. These groups helped to “fill in gaps” (P13) since participants found there was “ very little emotional [support] provided” (P10) by the healthcare system. In particular, participants felt that leaning on other migrants who had also been through the fertility treatment process in Israel was most helpful as they “ understand the culture shock with you” (P5). Facebook groups were also a “game changer” (P12), providing women with a community where their experiences were normalised and reassuring them that they were “ not alone” (P13).

“ … [leaning on] an immigrant is helpful because again, they know … what is a big deal versus what’s not a big deal, because you come from that shared immigrant background” P5

Many participants did not utilise support offered by the healthcare system since it was in Hebrew and they were more comfortable discussing emotions using their preferred language.

“ … there was a support group that I could join, within the system. I didn’t join it, because I knew it was going to be Hebrew, and I knew that just wouldn’t do it for me … as soon as I’m emotional the words cannot come out” P13

3. Impact of journey

Interplay with religion..

Certain aspects of the Jewish religion influenced participants’ experiences of their fertility treatment journey. Niddah may have been a cause of infertility for some participants since they may have not been able to go to the mikvah (ritual bath in which a woman must immerse at the end of niddah before she may resume physical intimacy with her husband) until after they had ovulated, thereby missing their chance to conceive that month.

“ The rabbis try to make it very lenient, so the problems that you’re having with bleeding and stuff don’t hinder your ability to have a child … they’ll make leniencies to go [to the mikvah] early so you can catch your ovulation.” P13

Some participants experienced fertility treatment to be a “ difficult time religiously” (P5). The laws of Niddah were particularly challenging.

“ … every single month you have like a reminder that you’re kind of failing at what you want to achieve and while you’re dealing with the emotions of that you also have this kind of like physical distance from your spouse … that also challenged, therefore, my relationship with God, because it felt like even more of a punishment” P1

Other participants took comfort from religion as it provided an explanation for their difficulties.

“… as a religious person, you have to like see some religious meaning in life … I have to assume that there’s a reason that we’re going through this craziness” P7

Relationships.

Whilst participants felt fertility treatment puts “stress on the couple” (P11), overall they had a “ deeper love and affection” (P4) for their partner.

“ … [participant’s husband] would be like I don’t want to do this anymore, it’s ruining our family, but I couldn’t give up … our struggles have sometimes pulled us apart a little bit, but they have also brought us together.” P13

Engaging within communities where those around them were having lots of children was challenging. Ultimately many participants turned “inwards” (P10) by withdrawing from their community and socialising less.

“ I live in a very like fertile community … when you’re going through treatment, and everybody else around you is pregnant, having babies, and you can’t get there … it’s too overwhelming” P13

Participants carefully selected who to disclose their journey to considering who would be “amazing and supportive” (P3). Although sharing helped participants access support, participants struggled with the need to provide updates regarding their treatment.

“ I didn’t really want anybody knowing my business because it would have felt like a monthly check in of like did it work, did it not work” P1

Personal impact.

The participants felt that their lives “revolved around the treatments” (P5) and the process forced them to put their lives “on a hold” (P2).

“ … my biggest frustration about the whole process … was how time consuming it is … you have a million and one tests to do. You’ve got so many doctors to run to here, there, and everywhere” P2

The treatment process placed a significant strain on participants’ lives, even impacting on their ability to keep jobs. Multiple participants felt grateful to be in jobs that gave “flexibility” (P13). Despite this, there was a perception of having “missed more work” (P7) going through the process in Israel rather than elsewhere due to the appointment burden, long amounts of time spent in waiting rooms before being seen and overall system inefficiency.

“ I’ve actually had to make choices over my career because of fertility treatments, I’ve had to turn down dream jobs, because I knew that I couldn’t do both at the same time” P13

Throughout the treatment process participants experienced an “emotional toll” (P13) and both “physically and mentally” (P3) did not feel like themselves. Particularly following treatment failures participants felt “guilty” (P6) or like a “failure” (P10). Participants had varying coping mechanisms throughout the process including finding “kid free spaces” (P5), changing their “attitude to stress” (P2) and making sure they kept “having fun” (P3). Overall, most participants had “trust in the health system” (P6), with one participant stating Israel is “ the best place in the world” (P13) for fertility treatment.

This study aimed to explore the experiences of English-speaking women who migrated to Israel and subsequently used largely state funded fertility treatment. The main findings were often influenced by socio-cultural factors. Such factors included the influence of others, systemic factors in the Israeli healthcare system and the overall impact of the journey on these women’s lives.

Since fertility treatment in Israel is largely state funded through compulsory universal national health insurance, women can continue treatment until they conceive without having to consider affordability [ 14 ]. In a healthcare system where women are required to pay for fertility treatment, a common reason for treatment discontinuation was financial problems [ 33 ]. However, in Israel, finances do not determine family size in the same way. The lack of a financial barrier to treatment in Israel has led to a culture of perseverance with fertility treatment whereby women undergo high numbers of treatment cycles [ 14 ]. This was reflected by the study’s sample with the majority of women utilising more than 5 treatment cycles.

The issue of bureaucracy within the Israeli healthcare system was repeatedly highlighted. Participants experienced the system to be confusing and there was a perception of acceptance towards failed cycles and a poor attitude to patient care. Many of these issues were accounted to the treatment being state funded resulting in IVF units being overrun [ 34 ]. Participants believed the treatment process would have imposed less of a burden on their lives in countries such as the US where fertility treatment is provided through private healthcare [ 35 ]. Despite the negative aspects of the treatment process, once participants were in the rhythm of treatment it was systematic and they appreciated the low cost incurred.

Globally research into the reproductive health of migrants focusses on those who have migrated due to wider issues, mostly humanitarian emergencies. This study’s population of English-speaking migrants to Israel did not face such challenges. Their main challenge resulted from contending with the ‘cultural imperative’ to have children at an already emotionally trying time [ 5 ].

In Israel all Jewish communities, including secular women, have higher fertility rates than their counterpart communities with similar levels of religiosity in the US [ 9 ]. A study found that the difficulties experienced by fertility patients in Israel are similar for women with no children or with one child and only improved for those women who had two children. This suggests that in Israel having just one child still does not relieve the stress of infertility [ 36 ]. Interestingly, data also show that fertility rate increases amongst migrants to Israel demonstrating the impact of the pronatalist culture [ 9 ].

Within a society whereby having children is expected infertile couples do not have the option to hide their struggle [ 37 ]. In alignment with this, participants experienced intrusive questions regarding their family planning that they did not believe would have been asked in their country of origin. An Israel-based study identified questions and social pressure about childbearing as being amongst the most prominent difficulties experienced by women during fertility treatment [ 36 ]. Participants withdrew from their community and spent less time with friends and family as socialising with those who were successfully having children was too challenging. This parallels literature that identified that for women who are unable to have children, spending time within their communities can emphasise the pain of being unable to conceive [ 38 ]. Furthermore, a study based in Israel identified social withdrawal as a coping mechanism during infertility treatment and one of the major correlates of distress [ 39 ]. Despite these struggles many participants received support from family and friends and identified utilisation of fertility treatment in Israel as normalised. This finding is unsurprising considering Israel has the highest rate of IVF per capita worldwide [ 40 ].

Whilst navigating fertility treatment in Israel participants relied on support from fellow migrants who were able to provide the most effective assistance as they best understood which aspects of the journey would present a culture shock. Other support came through online Facebook groups or worldwide organisations, such as UK based organisation Chana [ 41 ]. A study into stress in fertility treatment patients in Denmark found high levels of social support and disclosure of infertility to close relations reduced the stress associated with infertility [ 42 ]. These support networks were required since there was a lack of emotional support provided by the healthcare system. In a study into the perceptions of patient-centred care in Israeli IVF units, patients gave low scores for provision of emotional support [ 43 ]. Any available support through the healthcare system was in Hebrew, but this study found, in alignment with existing research, women are more comfortable discussing emotional topics in their preferred language [ 44 ]. An existing study regarding perseverance with fertility treatment despite failures advised centres providing treatment to improve provision of psychological support [ 14 ]. This is in contrast to countries such as the UK whereby the Human Fertilisation and Embryology Authority only licences clinics to provide fertility treatment if they also offer access to a counsellor [ 45 ].

Although all participants had at least a basic Hebrew language ability they were concerned about misunderstanding information in a healthcare setting. Accessing resources in English was challenging with more resources translated to Arabic or Russian. This is likely because 20% of the Israeli population are Arabs and most migrants to Israel since the early 1990s came from countries that were previously part of the Soviet Union [ 46 , 47 ]. The Israeli Central Bureau of Statistics identified 75% of immigrants to Israel in 2023 being from Russia or Ukraine [ 25 ]. No literature investigating English-speaking migrants’ experiences of navigating the Israeli healthcare system was found for comparison. Despite this, the participants’ experiences are supported by an internet search which showed Israel’s English Ministry of Health website is much more limited compared with the Hebrew website. When searching ‘fertility’ in Israel’s English Ministry of Health website 218 results appear compared with 1300 results when the Hebrew website is searched [ 48 ].

The attitude of impatience, rudeness and coldness within the Israeli healthcare system was different to the attitude of healthcare professionals that participants were used to in their native country. This poor bedside manner negatively impacted upon the participants’ experiences in alignment with a study that found decreased communication between nurses and patients leads to increased feelings of isolation [ 49 ].

Regardless of the religion in question, or level of religiosity, undergoing fertility treatment often presents unique religious and spiritual needs [ 50 ]. As all respondents were Jewish, undergoing fertility treatment within a system whereby many of the healthcare professionals understand the Jewish religion’s rules and cultures is unique to Israel and this improved the experience of participants. Existing literature explains that understanding of religious beliefs allows more culturally competent care [ 51 ]. Often participants used religion as a coping mechanism aligning with the finding that connectedness with God can reduce psychological distress [ 52 ]. Infertility also challenged faith in God. This concept is supported by an existing study which found religion gave women a positive outlet throughout their infertility journey. Despite this, the study identified that religious observance reduced with the number of infertile years experienced [ 49 ]. Overall, it is important to address the varying concerns that arise due to the interplay of religion and fertility treatment [ 53 ].

Strengths and limitations

This study began to address the gap in knowledge regarding the experiences of English-speaking women who migrate to Israel and use Israeli healthcare services. Whilst member validation was not possible due to time and resource constraints, coding of transcripts by two researchers and discussion of themes within the research team improved validity [ 54 ]. Data saturation was reached within the study’s population since no new codes or themes were identified by the 13th interview [ 55 ]. Nevertheless, due to a lack of variation within the population, saturation was not reached for the topic as a whole. Within the population there were varying levels of Hebrew language ability, country of origin and age of migration to Israel. However, all 13 participants were Jewish, of whom 10 were religious. Level of religiosity was likely to have influenced treatment experience. Furthermore, all participants had either had a live birth or were pregnant through fertility treatment at the time of interview which may have positively biased their retrospective accounts of their experience with fertility treatment. Recruitment of a diverse sample with women of any religion and any treatment outcome was attempted through advertisement in various Israel specific Facebook groups. Despite this, recruitment through Facebook prevented inclusion of ultra-Orthodox (Haredi) women as they tend to have limited internet usage. Furthermore, interviewing remotely allowed inclusion of women living across Israel. Since participants were required to have migrated to Israel, the lack of religious variation was likely a reflection of participants migrating under Israel’s Law of Return. This law entitles Jews to migrate to Israel [ 23 ]. Further research should target women from specific ethnic or religious groups to identify experiences of women from a wider demographic, including Arabs, Druze and other groups. Women who had not been successful with fertility treatment were unlikely to participate because talking about their experience would have been emotionally demanding. Since participants were required to speak English, they may have had different experiences of fertility treatment than migrants who speak other languages. Maintaining a reflexive approach and collaborating with a local researcher helped to mitigate the influence of cultural differences between the primary researcher (female, British, Jewish, undergraduate medical student) and the participants on findings.

Recommendations

Fertility treatment centres, in addition to organisations designed to support migrants in Israel such as Nefesh B’Nefesh, are encouraged to increase provision of English language resources [ 56 ]. Future research is recommended into identifying how best to support women who migrate to Israel in terms of navigation of the pronatalist society as well as the healthcare system itself. This research may be most effective if targeted at specific subsets of migrants, for example by level of religiosity or country of origin as they are likely to have different experiences and needs. Research into how the attitude of healthcare professionals impacts on the experience of fertility treatment and what can be done to improve these patient healthcare professional interactions is also worthwhile.

This study identifies the experiences of English-speaking women who migrate to Israel and access state funded fertility treatment. The findings begin to address the gap in the literature regarding the experiences of such women. Although all the participants had at least a basic Hebrew language ability and migrated to Israel optionally, they still faced challenges navigating the Israeli healthcare system as migrants. Interacting with healthcare professionals whose attitudes are unfamiliar and contending with a pronatalist society also negatively impacted upon the experiences of these women. Their experiences are improved by the minimal financial burden associated with treatment and the understanding of healthcare professionals regarding their religious beliefs. Barriers that these women face must be better anticipated. Improved provision of resources in English is essential and further research is needed into how women can best be supported whilst navigating Israel’s pronatalist society.

Supporting information

S1 checklist. inclusivity in global research checklist..

https://doi.org/10.1371/journal.pone.0309265.s001

S1 File. Topic guide.

https://doi.org/10.1371/journal.pone.0309265.s002

Acknowledgments

I would like to thank the participants for their involvement and time.

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