Saskia Briede

A patient’s perspective on care decisions 79 4 students (AB and NB respectively) after training. The interviewers did not have a former relationship with the participants and were not involved in the recruitment of patients in the previous study or the distribution of the patient education, to ensure patients could be as honest as possible in their evaluation. The interview guide contained straightforward questions about the content of patient education and open and flexible topics regarding feelings and expectations, allowing new or unexpected responses to be introduced (see supplementary appendix 2). In between the two rounds, the first set of interviews was analysed and the interview guide was adjusted according to these results. Main adjustments were removal of design-related questions (as we had sufficient input on that topic) and additional questions regarding patient perspective on the topic of care decisions, to explore this further. Interviews were conducted by phone to minimize burden for the participants and audio-recorded. 2.4 Data analysis All interviews were transcribed verbatim, anonymised and coded using NVivo 12 software. Collected data were analysed using reflexive thematic analysis with an inductive approach, meaning that the process of coding was data-driven [18–20]. Two authors (SB & NB) independently familiarised themselves with the data by reading and re-reading all transcripts. We used an iterative and flexible coding process. SB and NB identified, discussed, refined and revised codes regularly and when necessary a third author (TvC) was consulted until full agreement was reached. First theme development took place in multiple sessions with SB, NB and TvC with use of visual mapping to aid pattern formation and identification. In additional sessions with all four authors, themes were reviewed and refined. Throughout the process, we operated within a qualitative paradigm, corresponding to the “Big Q thematic analysis” described by Terry et al. [19] and kept the research questions in mind. Opposed to “small q thematic analysis”, often used in positivist research, “big Q thematic analysis” is characterised by theoretical independence and flexibility, and organic processes of coding and theme development. “The researcher is more like a sculptor, chipping away at a block of marble. The sculpture is the product of an interaction between the sculptor, their skills and the raw materials. Analysis becomes a creative rather than technical process, a result of the researcher’s engagement with the dataset and the application of their analytic skills and experiences, and personal

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