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Thematic analysis.



Until recently, thematic analysis (TA) was a widely used yet poorly defined method of qualitative data analysis. The few texts (Boyatzis, 1998; Patton, 2002), chapters (Hayes, 1997) or articles (Aronson, 1994; Attride-Stirling, 2001; Fereday & Muir-Cochrane, 2006; Tuckett, 2005) often came from outside psychology, and were never widely taken-up within the discipline. Instead, qualitative researchers tended to either use the method without any guiding reference, or claim some mix of other approaches (e.g., grounded theory and discourse analysis) to rationalise what essentially was TA. Our 2006 paper (Braun & Clarke, 2006) developed TA (in relation to psychology) in a ‘systematic’ and ‘sophisticated’ way (Howitt & Cramer, 2008, p. 341). TA is rapidly becoming widely recognised as a unique and valuable method in its own right, alongside other more established qualitative approaches like grounded theory, narrative analysis, or discourse analysis. TA is an accessible, flexible, and increasingly popular method of qualitative data analysis. Learning to do it provides the qualitative researcher with a foundation in the basic skills needed to engage with other approaches to qualitative data analysis. In this chapter, we first outline the basics of what TA is and explain why it is so useful. The main part of the chapter then demonstrates how to do thematic analysis, using a worked example with data from one of our own research projects – an interview-based study of lesbian, gay, bisexual and trans (LGBT) students’ experiences of university life. We conclude by discussing how to do thematic analysis well and how to avoid doing it poorly.
Thematic Analysis, p. 1
Thematic Analysis
Virginia Braun
The University of Auckland, Aotearoa New Zealand
Victoria Clarke
The University of the West of England, Bristol, UK
Key/index terms:
Code; Coding (Inductive; Deductive); Data familiarisation; Heteronormativity; Patterns;
Sexuality; Thematic analysis; Thematic map; Theme
Published as: Braun, V. & Clarke, V. (2012) Thematic analysis. In H. Cooper, P. M. Camic, D. L.
Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds), APA handbook of research methods in
psychology, Vol. 2: Research designs: Quantitative, qualitative, neuropsychological, and
biological (pp. 57-71). Washington, DC: American Psychological Association.
Thematic Analysis, p. 2
Until recently, thematic analysis (TA) was a widely used yet poorly defined method of qualitative
data analysis. The few texts (Boyatzis, 1998; Patton, 2002), chapters (Hayes, 1997) or articles
(Aronson, 1994; Attride-Stirling, 2001; Fereday & Muir-Cochrane, 2006; Tuckett, 2005) often
came from outside psychology, and were never widely taken-up within the discipline. Instead,
qualitative researchers tended to either use the method without any guiding reference, or claim
some mix of other approaches (e.g., grounded theory and discourse analysis) to rationalise
what essentially was TA. Our 2006 paper (Braun & Clarke, 2006) developed TA (in relation to
psychology) in a ‘systematic’ and ‘sophisticated’ way (Howitt & Cramer, 2008, p. 341). TA is
rapidly becoming widely recognised as a unique and valuable method in its own right, alongside
other more established qualitative approaches like grounded theory, narrative analysis, or
discourse analysis.
TA is an accessible, flexible, and increasingly popular method of qualitative data analysis.
Learning to do it provides the qualitative researcher with a foundation in the basic skills needed
to engage with other approaches to qualitative data analysis. In this chapter, we first outline the
basics of what TA is and explain why it is so useful. The main part of the chapter then
demonstrates how to do thematic analysis, using a worked example with data from one of our
own research projects – an interview-based study of lesbian, gay, bisexual and trans (LGBT)
students’ experiences of university life. We conclude by discussing how to do thematic analysis
well and how to avoid doing it poorly.
What is thematic analysis?
TA is a method for systematically identifying, organising, and offering insight into, patterns of
meaning (themes) across a dataset. Through focusing on meaning across a dataset, TA allows
the researcher to see and make sense of collective or shared meanings and experiences.
Identifying unique and idiosyncratic meanings and experiences found only within a single data
item is not the focus of TA. This method, then, is a way of identifying what is common to the way
a topic is talked or written about, and of making sense of those commonalities.
However, what is common is not necessarily in and of itself meaningful or important. The
patterns of meaning that TA allows the researcher to identify need to be important in relation to
the particular topic and research question being explored. Analysis produces the answer to a
question, even if, as in some qualitative research, the specific question that is being answered
only becomes apparent through the analysis. There are numerous patterns that could be
identified across any dataset - the purpose of analysis is to identify those relevant to answering
a particular research question. For instance, in researching white collar workers’ experiences of
sociality at work, a researcher might interview people about their work environment, and start
with questions about their typical work day. If most or all reported that they started work at
around 9am, this would be a pattern in the data, but it wouldn’t necessarily be a meaningful or
important one. However, if many reported that they aimed to arrive at work earlier than they
needed to, to chat to colleagues, this could be a meaningful pattern.
Thematic analysis is a flexible method that allows the researcher to focus on the data in
numerous different ways. With TA you can legitimately focus on analysing meaning across the
entire dataset, or you can examine one particular aspect of a phenomenon in depth. You can
report the obvious or semantic meanings in the data, or you can interrogate the latent
meanings, the assumptions and ideas that lie behind what is explicitly stated (see Braun &
Clarke, 2006). The many forms thematic analysis can take means that it suits a wide variety of
research questions and research topics.
Why thematic analysis?
The two main reasons to use TA are because of its accessibility and its flexibility. For people
new to qualitative research, TA provides and entry into a way of doing research that otherwise
can seem vague, mystifying, conceptually challenging and overly complex. It offers a way into
qualitative research that teaches the mechanics of coding and analysing qualitative data
Thematic Analysis, p. 3
systematically, which can then be linked to broader theoretical or conceptual issues. For much
qualitative research, the relationship is reversed. For example, to do discourse analysis (DA),
the researcher needs to first be familiar with complex theoretical perspectives on language (see
Potter, this volume) which invert the common-sense view of language as a mirroring reality –
instead, language is theorised as creating reality. Knowing this background is essential,
because it guides what the researcher ‘sees’ in the data, how they code and analyse them, and
the claims that are made. In contrast, TA is only a method of data analysis, rather than being an
approach to conducting qualitative research. We see this as a strength, as it ensures the
accessibility and flexibility of the approach.
TA offers a way of separating qualitative research out from these broader debates, where
appropriate, and making qualitative research results available to a wider audience. Its
accessibility as a method also suits multi-methods research being conducted by research
teams, where not everyone is a qualitative ‘expert’. TA also has a lot of potential for use within
participatory research projects – such as participatory action research (see Fine, et al. this
volume) or memory work (Onyx & Small, 2001) – where many involved in ‘analysis’ are not
trained researchers.
Flexibility and choices in thematic analysis
Linked to the fact that it is just a method, one of the main reasons TA is so flexible is that it can
be conducted in a number of different ways. TA has the ability to straddle three main continua
along which qualitative research approaches can be located: inductive versus deductive or
theory driven data coding and analysis; an experiential versus critical orientation to data; and an
essentialist versus constructionist theoretical perspective. Where the researcher locates their
research on each of these carries a particular set of assumptions, and delimits what can and
cannot be said in relation to the data, and how data can and should be interpreted (Willig, this
volume, provides a detailed discussion of these positions). Any researcher doing TA needs
actively to make a series of choices as to what form of TA they are using, and to understand
and explain why they are using this particular form (Braun & Clarke, 2006).
An inductive approach to data coding and analysis is a ‘bottom up’ approach, and is driven
by what is in the data. What this means is that the codes and themes derive from the content of
the data themselves – so that what is ‘mapped’ by the researcher during analysis closely
matches the content of the data. In contrast, a deductive approach to data coding and analysis
is a ‘top down’ approach, where the researcher brings to the data a series of concepts, ideas, or
topics that they use to code and interpret the data. What this means is that the codes and
themes derive more from concepts and ideas the researcher brings to the data – here what is
‘mapped’ by the researcher during analysis does not necessarily closely link to the semantic
data content.
In reality, coding and analysis often uses a combination of both. It is impossible to be purely
inductive, as we always bring something to the data when we analyse it, and we rarely
completely ignore the data themselves when we code for a particular theoretical construct – at
the very least, we have to know whether or not it’s worth coding the data for that construct.
However, one tends to predominate, and a commitment to an inductive or deductive approach
also signals an overall orientation which either prioritises participant- or data-based meaning, or
which prioritises researcher- or theory-based meaning. For this reason, inductive TA often is
also experiential in its orientation and essentialist its theoretical framework, assuming a
knowable world and ‘giving voice’ to experiences and meanings of that world, as reported in the
data. Deductive TA is often critical in its orientation and constructionist in its theoretical
framework, examining how the world is put together (i.e., constructed) and the ideas and
assumptions that inform the data gathered. However, these correspondences are not given, or
necessary. Consistency and coherence of the overall framework and analysis is what is
Thematic Analysis, p. 4
Braun and colleagues’ analysis of gay and bisexual men’s experiences of sexual coercion
provides a good example of a more inductive/experiential/essentialist form of TA, where
different ‘forms’ or modes of sexual coercion were identified from men’s reported diverse
experiences (Braun, Terry, Gavey, & Fenaughty, 2009). Clarke and Kitzinger’s (2004) analysis
of representations of lesbian and gay parents on television talk shows is a good example of
more deductive/critical/constructionist TA. This study drew on the concept of ‘heteronormativity’
to examine how participants in liberal talk show debates routinely invoke discursive strategies of
‘normalisation’, emphasising lesbian and gay headed-families conformity to norms of white,
middle class heterosexuality, as a response to homophobic/heterosexist accounts of lesbian
and gay parenting and its impact on children.
Like any form of analysis, TA can be done well, and it can be done poorly. Essential for
doing good thematic analysis are a clear understanding of where the researcher stands in
relation to these possible options, a rationale for making the choices they do, and the consistent
application of those choices throughout the analysis (further criteria are discussed later in the
chapter). We now provide a worked example that lays out how you actually do TA.
How to do thematic analysis – a worked example
We illustrate how to do TA using a worked example from an ongoing project which examines
sexuality, gender identity and higher education (Braun & Clarke, 2009; Clarke & Braun, 2009a).
Like many research projects, which evolve not just from identified ‘gaps’ in the literature, but
also from topics that grab us and pique our curiosity, this one developed as a result of our
experiences and reflections related to teaching and teaching training, as well as intellectual and
political questions about sexuality and gender identity in the classroom.
Part of the project involved interviewing 20 LGBT-identified students in New Zealand (10
students) and Britain (10 students) to understand their experiences of university life. Our worked
example of thematic analysis uses data from four of the British students. The students varied on
race/ethnicity (one British Asian; three white, one born in Europe), class (working or middle
class) and age (one ‘middle aged’), but were all studying social science subjects. The scope of
‘university life’ was broadly conceived, including the classroom, the curriculum and ‘hidden’
curriculum – the norms and ideas implicitly conveyed at university – interactions with course
peers and teaching staff, the campus and wider university environment, the local geographic
area and the local gay ‘scene’. In the semi-structured interviews, which lasted around an hour,
participants were all asked about their expectations of university life, whether they were ‘out’
(open) about their sexuality at university, their experiences of the classroom and the curriculum,
their views on LGBT lecturers coming out in the classroom, and, if they were studying a ‘people-
based’ discipline (Ellis, 2009), whether LGBT issues were included when relevant. Experiences
and perceptions of the wider campus environment and of student housing, interactions with
other students, friendship networks and social life, and the best and worst things about
university life as a LGBT student were also covered.
The interviews were audio-recorded and then transcribed orthographically, reproducing all
spoken words and sounds including hesitations, false-starts, cut-offs in speech (indicated by a
dash: e.g., thin-), the interviewer’s ‘guggles’ (‘mm-hm’s and ‘ah-ha’s), laugher, long pauses
(indicated by ‘(pause)’) and strong emphasis (indicated by underlining). Commas signal a
continuing intonation, broadly commensurate with a grammatical comma in written language;
inverted commas are used to indicate reported speech; three full-stops in a row (‘...’) signal
editing of the transcript. We have mainly edited for brevity, removing any words/clauses that are
not essential for understanding the overall meaning of a data extract. There are many different
styles of transcription (e.g., Edwards & Lampert, 1993) but if transcribing audio data for
thematic analysis, this level of detail is more than sufficient. As a general practice, we do not
advocate ‘cleaning up’ the transcript (such as making it more grammatical, or removing
hesitations, pauses and guggles) when working with data. Depending on your form of TA, such
Thematic Analysis, p. 5
details may be omitted from quoted data (if done, it should be noted), but as the details can be
revealing, we suggest working with a ‘full’ transcript while doing the analysis.
This topic, research question, and data collection method all suited TA. The research
question was experiential and exploratory, so our worked example illustrates a primarily
experiential form of TA, within a contextualist framework, which assumes truth can be accessed
through language, but that accounts and experiences are socially mediated (Madill, Jordan, &
Shirley, 2000). It illustrates a combination of inductive and deductive TA: inductive as we mainly
code from the data, based on participants’ experiences (meaning our analytic lens does not
completely override their stories); deductive as we draw on theoretical constructs from feminist
and queer scholarship like heterosexism (Adam, 1998), compulsory heterosexuality (Rich,
1980), heteronormativity (Warner, 1991), and the ‘hidden curriculum’ of heteronormativity
(Epstein, O'Flynn, & Telford, 2003) to render visible issues that participants didn’t explicitly
articulate. This means that the data are broadly interpreted within a feminist and a queer (e.g.,
Clarke & Braun, 2009b; Gamson, 2000) theoretical and ideological framework.
A six-phase approach to thematic analysis
The six-phases in our approach to TA (Braun & Clarke, 2006) are outlined and illustrated using
worked examples throughout. It is important to note that this is an approach to TA and to
learning to do TA. More experienced analysts will likely have deeper insights into their data
during familiarisation, find the process of coding quicker and easier and be able to code at a
more conceptual level, and more quickly and confidently develop themes that need less
reviewing and refining, especially if working with a smaller dataset. Writing is also likely to take a
more central place throughout analysis with more experience. The point we wish to emphasise
is that certain skills of analysis develop only through experience and practice. However, even
experienced researchers will draw and redraw lots of ‘thematic maps’ when searching for
themes, and engage in extensive review processes when working with larger datasets. A
thematic map is a visual (see Braun & Clarke, 2006) or sometimes text-based (see Frith &
Gleeson, 2004) tool to map out the facets of your developing analysis and identify main themes,
subthemes, and interconnections between themes and subthemes.
Phase 1: Familiarising yourself with the data
Common to all forms of qualitative analysis, this phase involves immersing yourself in the data
by reading and re-reading textual data (e.g., transcripts of interviews, responses to qualitative
surveys), and listening to audio-recordings or watching video data. If you have audio data, we
recommend listening to them at least once, as well as reading the transcript, especially if you
did not collect the data or transcribe them. Making notes on the data as you read – or listen – is
part of this phase. Use whatever format works for you (e.g., annotating transcripts, writing
comments in a notebook or electronic file, underling portions of data) to highlight items
potentially of interest. Note-making helps you start to read the data as data. Reading data as
data means not simply absorbing the surface meaning of the words on the page, as you might
read a trashy novel or magazine, but reading the words actively, analytically and critically, and
starting to think about what the data mean. This involves asking questions like: how does this
participant make sense of their experiences? What assumptions do they make in interpreting
their experience? What kind of world is revealed through their accounts? We will illustrate this
with a brief example from Andreas’ interview:
Andreas: let’s say I’m in a in a seminar and somebody a a man says to me ‘oh look at her’ (Int:
mm) I’m not going ‘oh actually I’m gay’ (Int: mm [laughs]) I’ll just go like ‘oh yeah’ (Int: mhm)
you know I won’t fall into the other one and say ‘oh yeah’ (Int: yep) ‘she looks really brilliant’
Our initial observations included: (i) Andreas reports a common experience of presumed
heterosexuality; (ii) coming out is not an obvious option; (iii) social norms dictate a certain
response; (iv) the presumption of heterosexuality appears dilemmatic; and (v) he colludes in the
presumption, but minimally (to avoid social awkwardness). Looking a bit more deeply, we
speculated that: (i) Andreas values honesty and being true to yourself; but (ii) he recognises a
Thematic Analysis, p. 6
socio-political context in which that is constrained; and (iii) walks a ‘tightrope’ trying to balance
his values and the expectations of the context. These initial observations suggest the data will
provide fertile grounds for analysis; reading Andreas’ answer as data reveals the richness that
can be found in even brief extracts of text. However, note that we did deliberately pick a
particularly rich extract; not all extracts will be as ‘juicy’ as this one, and you may have little or
nothing to say about some parts of your data.
The aim of this phase is to become intimately familiar with your dataset’s content, and to
begin to notice things that might be relevant to your research question. You need to read
through your entire dataset at least once – if not twice, or more – until you feel you know the
data content intimately. Make notes on the entire dataset as well as on individual transcripts.
Note-making at this stage is observational and casual rather than systematic and inclusive. You
aren’t coding the data yet, so don’t agonise over it. They would typically be a stream of
consciousness, a messy ‘rush of ideas’, rather than polished prose. Such notes are written only
to/for you, to help you with the process of analysis – think of them as memory aids and triggers
for coding and analysis. At most they may be shared among research-team members.
Phase 2: Generating initial codes
Phase 2 begins the systematic analysis of the data, through coding. Codes are the building
blocks of analysis: if your analysis is a brick-built house with a tile roof, your themes are the
walls and roof and your codes are the individual bricks and tiles. Codes identify and provide a
label for a feature of the data that is potentially relevant to the research question (Box 1 shows
an example of coded data). Coding can be done at the semantic or the latent level of meaning.
Codes can provide a pithy summary of a portion of data, or describe the content of the data –
such descriptive or semantic codes typically stay very close to content of the data, to the
participants’ meanings An example of this is ‘fear/anxiety about people’s reactions to his
sexuality’ in Box 1. Codes can also go beyond the participants’ meanings and provide an
interpretation about the data content. Such interpretative or latent codes identify meanings that
lie beneath the semantic surface of the data. An example of this is ‘coming out imperative’; this
code offers a conceptual interpretation to make sense of what Andreas is saying (see Box 1).
Some codes mirror participants’ language and concepts; others invoke the researchers’
conceptual and theoretical frameworks. For example, the code ‘not hiding, but not shouting’
stayed very close to the participants’ use of language (e.g., John said “I don’t make an attempt
to hide that I’m gay but at the same time I’m not very forward about it”). In contrast, the code
‘modifying behaviour... to avoid heterosexism’ invoked our frame of reference: no student
spontaneously used the term ‘heterosexism’ to describe their experiences, but we interpret their
accounts through this framework (Adam, 1998).
Codes are succinct and work as shorthand for something you, the analyst, understands;
they don’t have to be fully-worked up explanations – those come later. Codes will almost always
be a mix of the descriptive and interpretative. A novice coder will likely (initially) generate more
descriptive codes; as noted above interpretative approaches to coding develop with experience.
This doesn’t mean that interpretative codes are ‘better’ – they’re just harder to ‘see’ sometimes.
What is important for all codes is that they’re relevant to answering your research question.
Coding is something we get better at with practice.
TA isn’t prescriptive about how you segment the data as you code it (e.g., you do not have
to produce a code for every line of transcript). You can code in large or small chunks; some
chunks won’t be coded at all. Coding requires another thorough read of every data item, and
you should code each data item in its entirety before coding another. Every time you identify
something that is potentially relevant to the research question, code it! We say ‘potentially’
because at this early stage of analysis, you don’t know what might be relevant: inclusivity should
be your motto. If you are unsure about whether a piece of data may be relevant, code it. It’s
Thematic Analysis, p. 7
much easier to discard codes than go back to the entire dataset and recode data, although
some recoding is part of the coding process.
Once you identify an extract of data to code, you need to write down the code and mark the
text associated with it. You can code a portion of data in more than one way (as Box 1 shows).
Some people code on hard-copy data, clearly identifying the code name, and highlighting the
portion of text associated with it. Other techniques include using computer software to manage
coding (see Graesser & McNamera, this Handbook), or using file cards – one card for each
code, with data summary and location information listed – or cutting and pasting text into a new
word-processing file, created for this purpose (again, ensure you record where all excerpts
came from). An advantage of the latter methods is that you collate your coded text as you code,
but there is no right or wrong way to manage the physical process of coding. Work out what
suits you best. What is important is that coding is inclusive, thorough and systematic.
After you generate your first code, keep reading the data until you identify the next
potentially relevant excerpt: you then have to decide whether you can apply the code you have
already used, or whether a new code is needed to order to capture that piece of data. You
repeat this process throughout each data item, and the entire dataset. As your coding
progresses, you can also modify existing codes to incorporate new material. For example, our
code ‘modifying behaviour, speech and practices to avoid heterosexism’ was initially titled
‘modifying behaviour to avoid heterosexism.’ However, because students also reported
modifying speech and things like dress or self-presentation to avoid ‘trouble,’ we expanded this
code beyond ‘behaviour’ to make it better fit what participants said. It’s a good idea to revisit the
material you coded at the start, as your codes will have likely developed during coding: some
recoding and new coding of earlier-coded data may be necessary.
This stage of the process ends when your data are fully coded and the data relevant to
each code has been collated. Table 1 provides some examples of codes we generated from our
data, with a few data extracts collated for each code. Depending on your topic, dataset and
precision in coding, you will have generated any number of codes – there is no maximum. What
you want are enough codes to capture both the diversity, and the patterns, within the data, and
codes should appear across more than one data item.
Phase 3: Searching for themes
In this phase, your analysis starts to take shape as you shift from codes to themes. A theme
“captures something important about the data in relation to the research question, and
represents some level of patterned response or meaning within the data set” (Braun & Clarke,
2006, p. 82). Some qualitative researchers make reference to ‘themes emerging from the data,’
as if their dataset was a pile of crocodile eggs, and analysis involved watching the eggs until
each baby crocodile (theme) emerged, perfectly formed, from within. If only it were so easy.
Searching for themes is an active process, meaning we generate or construct themes rather
than discovering them. Although we call this phase ‘searching for themes’, it’s not like
archaeologists digging around, searching for the themes that lie hidden within the data, pre-
existing the process of analysis. Rather, analysts are like sculptors, making choices about how
to shape and craft their piece of stone (the ‘raw data’) into a work of art (the analysis). Like a
piece of stone, the dataset provides the material base for analysis, and limits the possible end-
product, but many different variations could be created when analysing the data.
This phase involves reviewing the coded data to identify areas of similarity and overlap
between codes: identify any broad topics or issues around which codes cluster? The basic
process of generating themes and subthemes, which are the subcomponents of a theme,
involves collapsing or clustering codes that seem to share some unifying feature together, so
that they reflect and describe a coherent and meaningful pattern in the data. In our data, we
noticed codes clustering around heterosexism and homophobia. Examining these in more
Thematic Analysis, p. 8
detail, we identified that the codes either focused on experiences of heterosexism and
homophobia, or responses to and ways of managing heterosexism/homophobia. We then
constructed one theme using all the codes relating to the participants’ experiences of
heterosexism/homophobia (e.g., ‘incident of (naming) homophobia/heterosexism’; ‘tensions in
relating to straight men’) and another using the codes relating to the participants’ management
of (actual and feared) heterosexism (e.g., ‘monitoring/assessing people and the environment for
the possibility of heterosexism’; ‘modifying speech, behaviour and practices to avoid
heterosexism’). The code ‘managing the heterosexual assumption by minimal agreement’ (see
Box 1) appeared to be a variation of the code ‘modifying speech, behaviour and practices to
avoid heterosexism’, and so it was incorporated into that theme.
A lot of codes also clustered around the issue of identity but didn’t form one obvious theme.
In this case, after exploring lots of different ways to combine these codes into themes and
drawing lots of thematic maps, we generated two themes: one around coming out and being
out; one around different versions of being a gay man. These provided the best mapping of the
identity data in relation to our research questions. A number of codes cut across both themes
such as the notion of ‘good gays’ (who conform to the norms of compulsory heterosexuality as
much as possible by being ‘straight-acting’ and 'straight-looking’, Taulke-Johnson, 2008) and
‘bad gays’ (who are ‘politically active and culturally assertive’, Epstein, Johnson, & Steinberg,
2000, p. 19). This example is not a case of undesirable overlap between themes; it illustrates
that certain concepts or issues may cut across themes and provide a unifying framework for
telling a coherent story about what is going on in the data, overall.
Another important element of this stage is starting to explore the relationship between
themes, and to consider how themes will work together in telling an overall story about the data.
Good themes are distinctive and, to some extent, stand-alone, but also need to work together
as a whole. Think of themes as like the pieces of a jigsaw puzzle: together they provide a
meaningful and lucid picture of your data. In your analysis, one central theme or concept may
draw together or underpin all or most of your other themes – for our example, this would be
During this stage, it can also be useful to have a ‘miscellaneous’ theme, which includes all
the codes that don’t clearly fit anywhere, which may end up as part of new themes, or being
discarded. Being able to ‘let go’ of coded material and indeed provisional themes if they do not
fit within your overall analysis is an important part of qualitative analysis. Remember, your job in
analysing the data, and reporting them, is to tell a particular story about the data, that answers
your research question. It isn’t to represent everything that was said in the data.
How many themes are enough, or too many? For our dataset, we generated six themes; for
brevity, only four are summarised in Box 2. Unfortunately, there is no magic formula that states
that if you have X amount of data, and you’re writing a report of Y length, you should have Z
number of themes! The more data you have, the more codes, and thus themes, you will likely
generate; if you are writing a longer report, you will have space to discuss more themes. But
with more themes, your analysis can lose coherence. What is essential is that your themes are
presented in sufficient depth and detail to convey the richness and complexity of your data –
you are unlikely to achieve this if you report more than six or seven themes in a 10,000 word
report. Your themes will likely be ‘thin’. If you’re trying to provide a meaningful overview of your
data, 1-2 themes are likely insufficient; they may be sufficient for an in-depth analysis of one
aspect of the data). In an 8-10,000 word article, we typically report 2-6 themes.
You should end this phase with a thematic map or table outlining your candidate themes,
and you should collate all the data extracts relevant to each theme, so you are ready to begin
the process of reviewing your themes.
Thematic Analysis, p. 9
Phase 4: Reviewing potential themes
This phase involves a recursive process whereby the developing themes are reviewed in
relation to the coded data and entire dataset. Essentially about quality-checking, it is particularly
important for novice researchers, and for working with very large datasets, where it is simply not
possible to ‘hold’ your entire dataset in your head. The first step is to check your themes
against the collated extracts of data, and explore whether the theme ‘works’ in relation to the
data. If it doesn’t, you might need to discard some codes or relocate them under another theme;
alternatively you may redraw the boundaries of the theme, so that it more meaningfully captures
the relevant data. If these tweaks don’t work, you might need to discard your theme altogether
and start again – you shouldn’t ‘force’ your analysis into coherence. Key questions to ask are:
Is this a theme (it could be just a code)? And if it is,
What is the quality of this theme (does it tell me something useful about the dataset, and my
research question)?
What are the boundaries of this theme (what does it include and exclude)?
Are there enough (meaningful) data to support this theme (is the theme ‘thin’ or ‘thick’)?
Are the data too diverse and wide-ranging (does the theme lack coherence)?
You may end up collapsing a number of potential themes together, or splitting a big broad
theme a number of more specific or coherent themes.
Once you have a distinctive and coherent set of themes that work in relation to the coded
data extracts, you should undertake the second stage in the review process – reviewing the
themes in relation to the entire dataset. This involves one final re-read of all your data to
determine whether your themes meaningfully capture the entire dataset, or an aspect thereof.
What you’re aiming for is a set of themes that capture the most important and relevant elements
of the data, and the overall tone of the data, in relation to your research question. If your
thematic map/set of themes provides this, good. You can move to the next phase. If not, further
refining and reviewing will be necessary to adequately capture the data. A mismatch will most
likely occur if selective or inadequate coding has taken place, or if coding evolved over a
dataset and data were not re-coded using the final set of codes. Revision at this stage might
involve creating additional themes, or tweaking or discarding existing themes.
Phase 5: Defining and naming themes
In defining your themes, you need to be able to clearly state what is unique and specific about
each theme – whether you can sum up the essence of each theme in a few of sentences is a
good test of this (see Box 2). A good thematic analysis will have themes which: (i) don’t try to do
too much, as themes should ideally have a singular focus; (ii) are related but don’t overlap, so
they aren’t repetitive, although they may build on previous themes; and (iii) directly address your
research question. Each theme identified in Box 2 has a clear focus, scope and purpose; each
in turn builds on and develops the previous theme(s); and together the themes provide a
coherent overall story about the data. In some cases, you may want to have subthemes within a
theme. These are useful where there are one or two overarching patterns within the data in
relation to your question, but each is played out in a number of different ways. Themes 3 and 4
could be described as subthemes of a broader theme of ‘managing gay identity’.
This phase involves the deep analytic work involved in thematic analysis, the crucial
shaping up of analysis into its fine-grained detail. As analysis now necessarily involves writing,
the separation between Phase 5 and 6 is often slightly blurry. This phase involves selecting
extracts to present/analyse and then setting out the ‘story’ of each theme with or around these.
What makes good data to quote and analyse? Ideally, each extract would provide a vivid,
compelling example that clearly illustrates the analytic points you are making. It’s good to draw
on extracts from across your data items to show the ‘coverage’ of the theme, rather than
drawing on only one data item (this can be frustrating when one source articulates it all perfectly
Thematic Analysis, p. 10
– the analysis in Box 3 quotes Asha because he expressed that part of the theme particularly
The extracts you select to quote and analyse provide the structure for the analysis – the
data narrative informing the reader of your interpretation of the data and their meaning. In
analysing the data, you use it to tell a story of the data. Data don’t speak ‘for themselves’ – you
mustn’t simply paraphrase the content of the data. Your analytic narrative needs to tell the
reader what about an extract is interesting, and why. Throughout your analytic section, you
would typically have at least as much narrative surrounding your data as extracts. Data must be
interpreted and connected to your broader research questions, and to the scholarly fields your
work is situated within. Some qualitative research includes this as a separate discussion
section; other research incorporates discussion of the literature into the ‘analysis’, creating a
‘results and discussion’ section. Both styles work with TA. An integrated approach works well
when strong connections exist with existing research, and when the analysis is more theoretical
or interpretative. It can also avoid repetition between results and discussion sections.
Box 3 shows part of the analysis of our theme managing heterosexism. It starts with a
general summary of the theme’s core issue, and then expands on this by providing specific
examples of different aspects of the theme, illustrated using brief extracts. Once sufficient detail
has been provided to show the scope of the theme, the longer extract offers rich and evocative
detail of what this actually meant for one participant. Analysis of that extract begins by
highlighting some data features which provide the basis for our interpretation around a broader
practice of minimisation and individualisation – a pattern across the dataset. There is an
interweaving of detailed and specific analysis of what happens in a particular data extract, and
more summative analysis that illustrates the broader content of the dataset in relation to the
theme. This reflects our combination of two broad styles of thematic analysis - descriptive,
where data tend to be used in illustrative ways, and conceptual/interpretative, where extracts
tend to be analysed in more detail, often for the latent meanings they draw on. Both offer
important analyses of data, and serve different purposes, but can usefully be combined, as we
show. The latter can be a more difficult form of analysis to grasp, because it moves from
surface/apparent meanings to latent/implicit meanings; it can take experience to learn to ‘see’
these in data.
However, even when we present a lot of short extracts of data, seemingly reporting quite
closely what participants said, the analysis always moves beyond the data. It doesn’t just report
words – it interprets them and organises them within a larger overarching conceptual
framework. Regardless of what ‘form’ of TA is done, analysis uses data to make a point.
Analysis needs to be driven by the question ‘so what?’ What is relevant or useful here to
answering my question? This process of telling an analytic narrative around your data extracts
needs to take place for all your themes. Each theme also needs to be developed not only in its
own right, but in relation to your research question, and in relation to the other themes.
Conclusions can and should be drawn from across the whole analysis. So an analysis needs to
make interconnections between themes and say something overall about the dataset.
The other aspect of this phase is working out what to call each theme. Naming might seem
trivial, but this short title can and should signal a lot. A good name for a theme is informative,
concise, and catchy. The name ‘‘mincing queens’ vs. ordinary guys who just happen to be gay’
(see Box 2) is memorable, and signals both the focus of the theme – different ways of being gay
– and something about the content of the analysis – that participants’ navigate between two
very different versions of being a gay man. ‘Mincing queens’ is also a direct quote from the data.
Using quotes in titles (also evident in themes 1-3) can provide an immediate and vivid sense of
what a theme is about, while staying close to participants’ language and concepts.
Thematic Analysis, p. 11
Phase 6: Producing the report
While the final phase of analysis is the production of a report such as a journal article or a
dissertation, it’s not a phase which only begins at the end. Unlike in quantitative research, we
don’t complete our analysis of the data and then write it up. Writing and analysis are thoroughly
interwoven in qualitative research – from informal writing of notes and memos to the more
formal processes of analysis and report-writing. The purpose of your report is to provide a
compelling ‘story’ about your data, based on your analysis. The story should be convincing and
clear, yet complex and embedded in a scholarly field. Even for descriptive TA, it needs to go
beyond description, to make an argument that answers your research question. Good writing
comes with practice, but try to avoid repetition, paraphrasing, unnecessary complexity and
passive phrasing. In general, qualitative research is best reported using a first person active
tense, but check the requirements for your report.
The order in which you present your themes is important: themes should connect logically
and meaningfully, and, if relevant, build on previous themes to tell a coherent story about the
data. We decided to use ‘compulsory heterosexuality at university’, which documents the
participants’ experiences of homophobia and heterosexism, as our first theme, because these
experiences, particularly the constant possibility, and fear, of heterosexism, shaped almost
every aspect of the students’ university life, and would be referenced throughout the rest of the
analysis. From there, it made sense to discuss the participants’ experiences of managing
heterosexism. We decided the two identity themes were the logical next step, as the theme
around coming out and being out closely related to the participants’ fear of heterosexism and
the ways in which they manage their practices to avoid heterosexism. The second identity
theme, which discussed different conceptualisations of gay identity, and participants’ desire to
be perceived as ordinary guys who just happen to be gay, had a less immediately obvious
connection to the first two, but linked well to the first identity theme.
Doing thematic analysis well
These guidelines lay out the process for producing a good thematic analysis that is thorough,
plausible and sophisticated. But like any analysis, TA can be done well, and it can be done
poorly. Common errors include providing data extracts with little or no analysis (no interpretation
of the data that tells us how this is relevant to answering the research question), or simple
paraphrasing or summarising data (see Braun & Clarke, 2006). Using data collection questions
as themes is another common error –themes are better identified across the content of what
participants say, rather than via the questions they’ve been asked. For instance, ‘incidents of
homophobia’ would be a weak theme, because it would involve simply describing different
things participants reported in response to an interview question on the topic. ‘‘There's always
that level of uncertainty’: Compulsory heterosexuality at university’ is a much stronger theme
because it captures something more complex about how the participants’ constant fear of
homophobia/heterosexism shaped their university lives, and incorporates data from across the
whole interviews, not just responses to specific questions about homophobia/heterosexism.
On a different level, an analysis can be weak or unconvincing if themes are not coherent or
try and do too much. Analysis can also suffer from lack of evidence. You need to provide
examples of, and analyse, enough data to convince the reader that this pattern you claim really
was evident – consider the balance of data and analysis in Box 3. A TA does have to relate to
patterns found across your dataset. This doesn’t mean every data item has to evidence each
theme, but it has to be more than idiosyncratic. Finally, TA can suffer due to mismatches
between the data and analysis, or between the form of TA done, and theoretical position of the
report (see Braun & Clarke, 2006, for more discussion of these, and for a checklist for doing
good TA). In developing and revising your analysis, make sure data-based claims are justified,
and the claims fit within your overall theoretical position (e.g., whether you are using an
experiential or critical form of TA).
Thematic Analysis, p. 12
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Thematic Analysis, p. 14
Box 1: Example of coded transcript (Andreas)
Transcript Codes
Andreas: ...I sometimes try to erm not conceal it
that’s not the right word but erm let’s say
I’m in a in a seminar and somebody- a a
man says to me ‘oh look at her’
Not hiding (but not shouting)
Heterosexual assumption
Hidden curriculum of
VC: mm
Andreas: I’m not going ‘oh actually I’m gay’ (Int: mm
[laughter]) I’ll just go like ‘oh yeah’ (VC:
mhm) you know I won’t fall into the other
one and say ‘oh yeah’ (VC: yep) ‘she
looks really brilliant’
Coming out is difficult (& not socially
Dilemmas created by the
heterosexual assumption
Managing the heterosexual
assumption by minimal agreement
VC: yep
Andreas: but I sorta then and after them you hate
myself for it because I I don’t know how
this person would react because that
person might then either not talk to me
anymore or erm might sort of yeah (VC:
yep) or next time we met not not sit next
to me or that sort of thing
Coming out imperative
Being a ‘happy, healthy’ gay man
It’s important to be honest &
Fear/anxiety about people’s reaction
to his homosexuality
Heterosexism is a constant possibility
Heterosexism = exclusion
VC: yep
Andreas: so I think these this back to this question
are you out yes but I think wherever you
go you always have to start afresh
Heterosexual assumption
VC: yep
Andreas: this sort of li-lifelong process of being
courageous in a way or not
Coming out is difficult (and not
socially normative)
Thematic Analysis, p. 15
Box 2: Definitions and labels for selected themes
1) "There's always that level of uncertainty": Compulsory heterosexuality at university. Maps
the participants’ experiences of (infrequent) homophobia and (constant) heterosexism and
highlights tensions experienced in relating to (straight) others, particularly people who are
common sources of heterosexism and overt homophobia (i.e., straight men; members of
religious and non-white groups), and feelings, or fear, of exclusion and not-belonging.
Heterosexism meant participants negotiated their sexual identities in an uncertain
environment, and experienced constant (but minimised) fear of people's reactions to their
sexuality. They had expected university students to be liberal and open minded, and were
surprised and disappointed they weren’t. But they felt this applied if you were ‘straight-
acting’, indicating university is a safe space only if you are a ‘good gay’. Participants’
experienced difficulty coming out at university, but also internalised and took responsibility
for these difficulties, rather than viewing coming out as something that is difficult because of
compulsory heterosexuality. Although participants expressed some anger about experiences
of overt homophobia, some homophobic and heterosexist ‘banter’ (e.g., anti-gay humour)
was acceptable if from friends - an indication that friends were comfortable with their
sexuality, but wasn’t acceptable from strangers. The heterosexual assumption and
compulsory heterosexuality were typically framed as a to-be-expected part of normal life.
2) "I don't go out asking for trouble": Managing heterosexism. Outlines the ways the
participants modified their speech, behaviour and practices to avoid heterosexism and
homophobia, and continually monitored people and the environment for evidence of potential
heterosexism or homophobia. They constantly weighed up whether it was safe to come/be
out with a particular person or in a particular space. The participants typically assumed
responsibility for managing heterosexism (they don’t ‘ask’ for trouble) and accepted this as a
normal part of life. They seemed to lack a sense of entitlement to live free from heterosexism
and a political and conceptual language with which to interpret their experiences of
heterosexism and homophobia.
3) "I'm not hiding, but I'm not throwing it in people's faces": Being out (but not too out) at
university. Focuses on the degree to which the participants were out and open about their
sexuality at university, and the management of sexual identity amidst competing pressures
to be a 'happy, healthy gay' (comfortable with and open about their sexuality, with a ‘fully
realised’ gay identity) and a ‘good gay’ (not too ‘overt’; not 'forcing' their homosexuality on
4) Mincing queens vs. ordinary guys who just happen to be gay. Focuses on participants’
resistance to a gay identity as a ‘master status’(Becker, 1963), an identity that overrides all
other identities – they wanted to be seen as an ordinary guy who just happens to be gay.
They took responsibility for carefully managing other people's perceptions of their sexual
identity, acutely aware that it takes very little to be judged as 'too gay' (a ‘bad gay’). They felt
very limited by popular conceptions of gay men and worked hard to distance themselves
from image of the camp gay man, the ‘mincing queen’, the Sex and the City gay best friend,
the gay style guru...
Thematic Analysis, p. 16
Box 3: Report of theme 2: "I don't go out asking for trouble": Managing heterosexism
In common with others (e.g., Taulke-Johnson & Rivers, 1999), our participants described
monitoring and assessing people and the environment for evidence of potential
heterosexism, weighing up whether it would be safe to come and be out. They decided not
to come out when people made overtly anti-gay comments. Asha, for instance, took the
comment ‘one thing I just can’t understand is gay people’ as strong evidence of a potential
negative response to his coming out, and chose not to. They made decisions to come out
when people discussed gay-related issues in a broadly positive way, mentioned gay friends,
or expressed ‘gay friendly’ sentiments (e.g., ‘want[ing] to be the ultimate personal fag hag,’
This monitoring was sometimes a relatively passive process (‘I just picked up tell tale
signs about it’, Asha); at other times, participants actively ‘test[ed] the waters’ (David) and
‘tr[ied] and manipulate the conversation to head in that direction and see how to respond to
it’ (Asha). Asha described this rather evocatively:
Asha: just basically erm er, does he have a gay friend? Yes or no, is he alright with a gay
friend? Yes or no. This person is alright to go out with- you know to come out with
and basically if the answers are different the questions are different and the
outcomes would be different... you’re just trying to you know answer all the questions
to see what the outcome is and it’s kinda a bit of a headache
VC: It sounds exhausting, and stressful
Asha: It is, very much so but it’s kinda something that I have in the back of my mind... I find
out you know which box they tick, which box they don’t tick and if they tick the right
ones or if they tick the wrong ones I know what action to take from there...
VC: Yep yep, god that sounds very hard
Asha: Well the thing is it’s almost kinda- I wouldn’t, I don’t know it’s something that just
happens in the background you know- I hardly notice it
VC: Yeah like this processing that going on and kinda churning away
Asha: Yeah all these things that you just happens that you’re not even completely aware of
but it’s building up and you know you look back at it you see all these point and you
say to my- you say to yourself right ‘I’m gonna tell this person I’m gay’ ‘I’m gonna’
you know and yeah
After initially agreeing with the interviewer, VC’s, assessment that this is an ‘exhausting
stressful process’ (‘It is, very much so’), Asha described it as a more sub-conscious process,
something he ‘hardly notice[d]’. When VC again suggested it sounded “very hard”, he offered
no agreement. Despite his detailed and vivid account, Asha appeared invested in framing
this as a mundane rather than negative, and therefore “hard”, process. This ‘minimising the
negative’ approach was common: the participants consistently framed phenomena that could
be read as evidence of heteronormativity and instances of prejudice (Taulke-Johnson, 2009)
as to-be-expected parts of normal life.
Asha earlier vividly described this process in a way which suggested it was negative, yet
implicitly located the problem within his own psychology rather than the environment:
Asha: constantly monitoring, keeping an eye out, keeping an ear out just you know, the little
checklist this worst case- or not a worst case scenario but you’re having a list in your
mind of all the possible things that can go wrong and you- you’re always going over
that list of all the things that could go wrong I’ve kinda built- well personally for me it
builds on my paranoia
In describing himself as paranoid, Asha suggests his response, rather than a
heterosexist context, is at fault. All the participants interpreted difficulties they experienced in
navigating a heterosexist world in this way. John, for example, associated his difficulties with
Thematic Analysis, p. 17
coming out with his personality (he got embarrassed, and feared getting and looking
embarrassed) rather than with the inherent difficulties that can exist around coming out (see
DeCrescenzo, 1997; Flowers & Buston, 2001; Markowe, 2002) in heterosexist contexts. In
internalising their response to heteronormative contexts thus, responsibility for change is
located within the participants, making it a personal rather than a political issue.
The degree to which students implicitly accepted responsibility for managing
heterosexism to avoid ‘trouble’ (David) by constantly modifying their speech, behaviour and
other practices was the most striking feature of how they navigated the university climate.
They had a strong sense that behaving or speaking in certain ways (being a ‘bad gay’,
Taulke-Johnson, 2008) invited ‘trouble,’ and placed the onus on themselves to avoid it and
protect themselves: ‘you have to sort of be very careful how you sort of came across to
people’ (David). The participants censored their speech and behaviour (‘tell... half of the
truth’, Andreas); avoided coming out or making ‘overt’ displays of homosexuality, such as by
showing affection to a same-sex partner, being too camp and acting like ‘a mincing queen’
(John), or wearing ‘obviously gay’ clothing; and avoided certain people (‘groups of lads’,
John) and areas. Campus and city were seen as safe ‘as long as you took the measures –
you know as long as you’re sensible about it you don’t go throwing it in people’s faces you
don’t go down to you know places like [predominantly working class/non-white city suburb].’
(Asha). [analysis continues]
Thematic Analysis, p. 18
Table 1: Six codes with illustrative data extracts (direct quotes)
Modifying speech,
behaviour and
practices to avoid
Tensions in
relating to
straight men
Incident of
Fear/anxiety about
people’s reactions
to his sexuality
Managing the
assumption by
minimal agreement
people/the environment
for the possibility of
I’m not somebody
that goes out
looking for trouble...
so you don’t want
to necessarily go
down that road, so
you sort of make
up some- not
make up some
story, but you only
tell sort of half the
truth (Andreas)
I would feel fine
going clubbing [to
a straight club]
with my boyfriend
but I’d be very
wary of making it
obvious (John)
if I’m out with my
boyfriend and it’s
late at night and
we’re sort of
walking home and
we’ll sort of holding
hands and… if it’s
like mostly girls
and stuff and that’s
I know if I go into a
lecture hall and I’m
like on my own
without a group
some of the lads
are a little bit less
inclined to sort of
sit with you in a
way... (David)
that’s the old thing
that it’s sort of
easier in a way to
be out with females
than with sort of
you know blokey
blokes (Andreas)
I did have quite a-
an interesting
conversation with
one guy… at the
end of the
conversation… he
goes… ‘you’re an
actual really nice
guy aren’t you? ‘Cos
I wasn’t really over
sure about you
when we first
started, ‘cos you
could tell you were
this one guy drunk
just came along
and just started
telling me to my
face I was sick that
there was
something wrong
with me, there was
something wrong
with us and we
should [f**k] the
hell out of there...
I have once seen a
group of lads
standing outside
one of the [gay]
bars like jeering
and stuff... (John)
There’s this one
person from work
who’s extremely
religious, and I
don’t mention it [my
whatsoever, he did
mention one story
that er gay people
were cursed by the
god and turned into
I’d just hate to see
what my dad would
do (Asha)
I was a little bit
worried about how I
was treated, I didn’t
want to go out and
start helping them in
shoe shops...
I do remember being
a bit worried about
who I’d end up living
with because I opted
for a a student
house and that’s five
random people
thrown with you
I was asked... ‘why
did you come from
another country to
Bristol?’, if you er go
into this er spiel
about ‘oh there was
somebody involved’
then you’re close to
‘who was it then?’...
you never know how
I realise and notice
that I sometimes try
to erm not conceal it,
that’s not the right
word, but erm let’s
say I’m in... seminar
and somebody- a a
man says to me ‘oh
look at her’ I’m not
going ‘oh actually
I’m gay’ I’ll just go
‘oh yeah’ you know I
won’t fall into the
other one and say
‘oh yeah she looks
really brilliant...’
I don’t agree but I
don’t disagree, I kind
of erm, I probably
just say ‘yeah she-’
What would I say?
Probably something
like ‘oh she looks
okay’ or ‘yeah she
looks nice’ but I
wouldn’t say ‘oh
yeah like I wanna
(laughs) I wanna do
her’ or something
just how much I know
them... there’s a lot of
people I wouldn’t go
into great detail with
about what I get up to
and stuff, whereas
other people I would,
yeah I suppose I like
to feel reasonably
safe when telling them
stuff like that (John)
erm I just remember
him making some kind
of comment to me on
the bus to London
about Earl’s Court and
gay art or something
and er yeah, and I just I
didn’t think that he’d be
the sort of person that’d
be that bothered by
things like that you
know what I mean
you go to a party
where you don’t know
anybody... and ‘oh let
me introduce you to
so and so’ and then
Thematic Analysis, p. 19
okay but if a group
of lads were
coming like we
would loosen up or
go via like a
different route
with other Asians
as well... I wouldn’t
say probably I
would just shut up
gay as soon as you
walked through the
door’… my reaction
was ‘get knotted’
sort of thing and just
walked off ‘cos I
thought you know
that shouldn’t be a
issue (David)
monkeys (Asha)
I had a couple of
incidents where all
of sudden when
you then say ‘I’m
gay’ then it’s this
(pause) you know
erm wink wink
nudge nudge thing
sort of these jokes
people react
if I came out there I
probably would have
been lad bait so I
decided to keep it to
myself... I had an
idea of what kind of
response I would get
and so just sensible
decision of just
keeping my mouth
shut (Asha)
like that (John)
I was asked ‘what
are you doing then
in Bristol?’... ‘was it
a nice girl?’ so you
don’t want to
necessarily go down
that road so you...
only tell sort of half
the truth (Andreas)
you sort of after a
while you start this
there’s always testi-
testing can I not can I
tell that- but I mean
what will happen if I
tell will people then
immediately say oh
sorry mate I need a
drink’ (Andreas)
... A deductive thematic analysis of transcripts was carried out independently by two of the authors (MLT and RE) using a modified version of the methodology described by Braun and Clarke [12][13][14] and adhering to the Consolidated Criteria for Reporting Qualitative Research [15]. This followed the key steps of familiarization, coding, development of themes, validating (and ensuring reliability), defining and name themes (Fig. 1). ...
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Background Surgeons are commonly evaluated by surgical skills and outcomes rather than their character traits. We sought to examine role model behaviours of senior surgeons through the lens of Aristotelian (virtue) ethics. Methods Semi-structured focus group interviews were undertaken of anaesthetic trainees at a large university hospital NHS Foundation Trust and transcripts were subjected to thematic analysis to yield themes and subthemes. Participation of the trainees was entirely voluntary and focus groups were conducted using Zoom™. Results The overarching themes identified were ‘Teamwork makes the dream work’, ‘Captain of the ship’ and ‘Strong foundations’. Conclusion We hope to take lessons learnt in conjunction with our previous work to help refocus surgical training towards a process of character reformation, rather than simply imparting technical skills to trainees.
A meta-synthesis of qualitative research was conducted on co-teaching by general and special educators working with students with and without disabilities in primary and secondary general education classrooms. We sought to update the Scruggs et al., 2007 meta-synthesis to discern new knowledge, including co-teaching's impact on students and teachers. Although challenges are identified, co-teachers perceive that co-teaching can enhance their and their students' learning. Findings suggest that school personnel, researchers and policymakers can consider co-teaching as a learning context for co-teachers as well as a dynamic framework that can potentially foster effective instruction for all students in the co-taught classroom.
Introduction At the onset of COVID-19 diagnostic radiographers from Gauteng, South Africa, shared their experiences of the new workflow and operations, their well-being and their resilience during this time. They experienced emotional, physical and financial fatigue. It is now over two years later, and South Africa has experienced four waves of COVID-19. Therefore, this study explored diagnostic radiographers' experience of COVID-19 after two years and four waves. Methods A qualitative explorative, descriptive and contextual study was conducted collecting data through nine virtual individual in-depth interviews. Responses from the diagnostic radiographers in Johannesburg, Gauteng South Africa, underwent thematic analysis. Results Thematic analysis revealed two themes and related categories. Theme one: participants shared synchronistic experiences with the four COVID-19 waves, the heterogeneous vaccination ideologies and their support and coping skills. Theme two: lessons learnt and the way forward. Conclusion Participants shared feeling overwhelmed at the onset of COVID-19 and feared infecting their family, friends and colleagues. However, their anxiety and fear decreased with time. They experienced the Delta variant as the worst and felt supported by their colleagues more than by management. They recounted observations of vaccine hesitancy but acknowledged that vaccination had alleviated some of the fear and anxiety. Participants' coping skills varied, and reflecting on their experience, they shared the lessons learnt and the way forward.
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Background falls in care homes are common, costly and hard to prevent. Multifactorial falls programmes demonstrate clinical and cost-effectiveness, but the heterogeneity of the care home sector is a barrier to their implementation. A fuller appreciation of the relationship between care home context and falls programme delivery will guide development and support implementation. Methods this is a multi-method process evaluation informed by a realist approach. Data include fidelity observations, stakeholder interviews, focus groups, documentary review and falls-rate data. Thematic analysis of qualitative data and descriptive statistics are synthesised to generate care home case studies. Results data were collected in six care homes where a falls programme was trialled. Forty-four interviews and 11 focus groups complemented observations and document review. The impact of the programme varied. Five factors were identified: (i) prior practice and (ii) training may inhibit new ways of working; (iii) some staff may be reluctant to take responsibility for falls; (iv) some may feel that residents living with dementia cannot be prevented from falling; and, (v) changes to management may disturb local innovation. In some care homes, training and improved awareness generated a reduction in falls without formal assessments being carried out. Conclusions different aspects of the falls programme sparked different mechanisms in different settings, with differing impact upon falls. The evaluation has shown that elements of a multifactorial falls programme can work independently of each other and that it is the local context (and local challenges faced), which should shape how a falls programme is implemented.
Background: Many institutions in undergraduate medical education have developed unique curricula to teach social determinants of health (SDOH). Geographic information system (GIS) mapping is one tool that learners could use to understand our built environment and its correlation with health outcomes through data analysis, visualization and active learning. Approach: At the University of North Carolina School of Medicine, medical students participate in a 4-year longitudinal curriculum on social and health systems science with the final year dedicated to self-directed learning. This final year course incorporates a GIS-based online module to help students apply their understanding of the health impacts of SDOH. Students create online maps with simulated patient data and identify 'hotspots' with map overlays using ArcGIS software. Students write reflections on their maps based on the implications of SDOH. Thematic analysis of these reflections identified patterns within the narrative data. Evaluation: From March 2020 to February 2021, 148 fourth-year medical students participated in the GIS learning module. Five major themes were identified: Explored Social Determinant Topics, Inclusion of Geo-mapping in Curriculum, Utility of Geo-mapping in Healthcare, Future Application of ArcGIS for Personal Use, Impressions of ArcGIS Software. Students showed engagement and interest in the exercise, and responses were overall positive. Responses showed understanding of the application of ArcGIS and demonstrated knowledge of social determinants of health. Implications: A self-directed, active learning online module using GIS mapping offers a generally popular, eye-opening and unique method for teaching SDOH in undergraduate medical education.
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Research fellowship programs help medical graduates acquire research skills for an academic career. While our institution employed a large number of research fellows, it did not offer them a formal training program. This study aimed to assess the views of fellows and their mentors regarding the current research fellowship program, and to seek their suggestions for a formal training program at our medical center. We conducted a qualitative descriptive study using both focus group discussions and individual interviews with research fellows, and individual interviews with their mentors. We recruited all eligible participants by email. We collected data in person and analyzed it thematically. We followed the consolidated criteria for reporting of qualitative research (COREQ) checklist. A total of 17 research fellows and 17 mentors participated in the study. Participants described the current non-formal program and proposed suggestions for a formal training program. The identification of available vacant positions and the recruitment process followed an unstructured approach, through networking with mentors and previous fellows. Although there is a formal contract, there is no job description, and no definition of roles, responsibilities and rights. Some fellows get the opportunity of being involved in all aspects of research and benefit from a favorable mentor-mentee relationship. Conversely, others struggle with authorship and with the projects allocated to them, some being "non-research" related. Not all fellows end up publishing their projects. Participants provided suggestions to shift into a formal training, including measures to improve on the recruitment process of fellows, defining roles and exposure to all aspects of research. Research fellows are eager to learn, but the currently available program is unstructured. They need a formal training program that meets their expectations, one that offers equitable learning opportunities and benefits to all.
Informed by a theoretical discussion on teaching global competence, this case study explored how to use inquiry learning to foster Chinese teacher candidates’ acquisition of global competence. The results showed that inquiry learning helped student participants understand glocalised educational practices, perspectives, and ideologies; embrace global and intercultural engagement; and grasp the skills and dispositions to critically examine global and local educational issues, power discourses, and work for sustainable educational development. This study suggests pedagogical and programme improvement suggestions, including ensuring instructor guidance in inquiry learning and intercultural engagement, equipping students with critical sociocultural inquiry capabilities, and constructing international interaction platforms.
This research discusses the development of academic-practitioner partnerships in Forensic Science and examines the opinions and experience of those involved in the field. An anonymous online survey was completed by 56 participants who work in the field of forensic science. The questions related to their work experience, their experience of research and partnership, and their opinions on the benefits and barriers that exist. The results were analysed using a mixed methods approach, with quantitative analysis of the responses to closed questions using two-way chi-square statistical analysis, and qualitative analysis of the free texts response using reflexive thematic analysis. The work identifies that there is demand for partnership, the perceived benefits and barriers that exist to partnership, and establishes how the role of the participant (academic, pracademic or practitioner) impacts upon their view of partnership. We include the term pracademic to mean an individual who has worked as a practitioner and an academic, not necessarily simultaneously. Quantitative analysis identified that there was very little statistically significant difference in the responses between groups. Pracademics considered that ‘institutional and cultural’ and ‘lack of the respect of the other role’ were more significant barriers than the other groups. Association was also found between those with greater experience of research and the view that partnership ‘improved legitimacy in practice’ and ‘increased legitimacy of research’. There was also statistical significance in those with more than average experience of partnership who identified ‘improved legitimacy in practice’ as a benefit of partnership. Reflexive thematic analysis of free text comments identified a need and demand for partnership with three key themes developed as being necessary for successful partnership. These are the ‘three ‘R’s’ – the need for effective communication and the development of a Relationship; the Relevance of the partnership to the participants role; and the inclusion of personal Reward such as improved practice or better research.
Organizations can invest significant time, energy, and resources initiating food waste reduction practices. Addressing the critical success factors for a new initiative or change in practice is vital. This research aimed to identify the overarching critical success factors for food waste reduction practices across the food supply chain. Semi-structured interviews were held with 41 participants representing organizations from farm to fork in Aotearoa New Zealand. Eighteen critical success factors, grouped into five overarching themes emerged: (1) communication and collaboration, (2) motivation and engagement, (3) robust data, (4) sustainable management systems, and (5) external influences. Of the success factors identified, the majority aligned with success factors identified for the health promotion and ‘green’ business sectors. Comparisons suggest that organizations should initially focus their planning process on collaboration with other agencies, robust data collection, engagement throughout the organization, and determining a positive cost-benefit analysis. The general alignment of critical success factors across disciplines enables the food waste sector to move quickly to support successful food waste reduction practices, with corresponding environmental, social, and economic benefits.
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