Data generation and analysis
MAY
From May 2019 to February 2022, the lead author met every 6 months with 12 study participants (four from each city). Seven participants were receiving rent subsidies and mentorship, and five were only receiving rent subsidies. We made the decision to follow a small number of participants over an extended period of time to get a deeper sense of how the mentorship relationships were evolving over time. There were also pragmatic considerations given participants lived in three different cities and the initial intention (prior to pandemic-related restrictions) was to conduct all the interviews in-person. We chose to include participants in both the intervention and the control group because we wanted to compare/contrast their experiences and explore whether there may be factors There were 24 in-person interviews conducted before pandemic-related restrictions required changing to video or phone for the other 47 interviews (one person chose not to participate in their final interview). Interview questions were loosely structured and conversational in nature, focused on socioeconomic inclusion and questions that arose from analysis of prior interviews. Field notes were written immediately after each interview and contained a mixture of what could not be captured through audio recordings (e.g., participant surroundings and a general sense of their well-being) and preliminary analytic insights. All interviews were audio-recorded and then transcribed by a member of the research team.We used reflexive thematic analysis to help us make sense of the qualitative data (Braun and Clarke, During the team meetings, we discussed what was and was not (Kawabata & Gastaldo, There is a common understanding among qualitative researchers that the main research question one goes into the study with might end up being different than the one that actually gets answered; the question is more like a “compass” than an “anchor” (Eakin & Mykhalovskiy, 