Table 2 Braun’s (2006) Thematic Analysis Approach adapted from Braun et al. 2006 [28]

1. Familiarisation with the data

Reading and re-reading the data, to become immersed and intimately familiar with its content

2. Coding

Generating succinct labels (codes) that identify important features of the data that might be relevant to answering the research question. It involves coding the entire dataset, and after that, collating all the codes and all relevant data extracts, together for later stages of analysis.

3. Searching for themes

Examining the codes and collated data to identify significant broader patterns of meaning (potential themes). It then involves collating data relevant to each candidate theme, so that you can work with the data and review the viability of each candidate theme.

4. Reviewing themes

Checking the candidate themes against the dataset, to determine that they tell a convincing story of the data, and one that answers the research question. In this phase, themes are typically refined, which sometimes involves them being split, combined, or discarded.

5. Defining and naming themes

Developing a detailed analysis of each theme, working out the scope and focus of each theme, determining the ‘story’ of each. It also involves deciding on an informative name for each theme.

6. Writing up

Weaving together the analytic narrative and data extracts and contextualising the analysis in relation to existing literature.