## 2 Materials and Methods
### Data Collection and Analysis  
All interviews were recorded using a Dictaphone device, seven of the interviews being conducted in person at the subject's place of work, and one being conducted online using Microsoft Teams (Microsoft Corporation, Washington, DC, USA). Following the semi-structured interview guide afforded the flexibility for topics to be discussed in a natural conversational manner whilst ensuring that the main areas of interest were covered by all subjects and probed to elicit detailed responses. The interviews lasted between 33 and 58 min (\(M\) = 45.8 min \(\pm\) 9) and were concluded once each subject felt they had no additional contributions to offer. The semi-structured interview guide is shown in Table 1.  
Reflexive thematic analysis (RTA) was used to interpret, organize, and report the interview data [34]. Importantly, this approach recognizes the contextualized nature of knowledge, that is, accepting multiple realities and situated meaning. As such, researchers are a valued resource during the analytical process who draw upon their knowledge and experience to facilitate the data interpretation [35; 36]. In this way, data saturation was not considered as an a priori criterion, since RTA is posited by [PERSON] and [PERSON] [37] as an iterative and evolving process in which meaning is actively generated rather than passively identified within the dataset. Given this, the potential for new insights remains open-ended, making data saturation conceptually incompatible with RTA. Instead of aiming for saturation, we made situated and reflexive decisions about sample adequacy, prioritizing the richness and complexity of the data in relation to our research aims. The interview recordings were transcribed verbatim, disidentified by replacing participant names with a number, and then analyzed following the six-stage process outlined by [PERSON] and [PERSON] [38]. Specifically, the first stage focused on becoming intimately familiar with the dataset; in this regard, listening to the interview audio recordings several times during manual transcription of the data proved useful. The second phase aimed to generate data codes, which were semantic (i.e., descriptive), shorthand labels for relevant pieces of information. The third phase aggregated all the data codes to generate latent themes. For clarity, while the semantic codes are more descriptive in nature and take information at face value, the latent themes intend to represent collective meanings, requiring a more analytical process. Furthermore, while the themes are distinct and even contradictory, when taken together, they should produce a coherent representation of the dataset [39]. As this relates to the present study, the themes identified during this step are referred to as 'lower-order themes'. The next step involved reviewing the themes to ensure that each appropriately reflected the raw and coded data and contributed to the overall narrative; at this stage, themes might be removed altogether, or several might be combined if another level of latent meaning was identified; this process generated the 'higher-order themes'. The fifth step defined and named the higher-order themes, which involved the regression of higher-order themes, through lower-order themes, data codes, the raw dataset, and the research question(s) to ensure a coherent line of analysis. When this process was complete, the higher-order themes' names were reviewed for accuracy and coherence before being finalized. The final step was to 'produce the report', or in other words, write the analysis. Despite this being presented as the final stage in RTA, it should be noted that writing and the development of a narrative in qualitative analysis tend to occur throughout the process [40], as was the case in the present study. Similarly, although these six stages have been presented sequentially, it is common and expected (inherently given the term reflexive) that researchers move between them flexibly to ensure analytical rigor [34].