Data collection and analysis

The project coordinator at each site collected administrative data and reported the data to WAP quarterly (see Appendix F for example of program report required by WAP). WAP then provided these data to the Principal Investigator (PI). Sites reported data on the number of PrEP prescriptions written for the year prior to program

implementation to WAP in the sites’ application for program grants. I maintained the administrative data in a password-protected Excel spreadsheet. Site staff collected administrative data from January 2017 through December 2017; I received all program data for each site by July 2018. I reviewed the administrative data as they were provided and discussed any questions with the project coordinator.28

3.3.1.1 Aims 1.2 and 2

The program coordinator at each clinic provided a list of potential participants to contact for recruitment. I asked program coordinators to include both staff29 _and

providers involved in program implementation. These staff and providers included both facilitators and recipients, as defined by the i-PARIHS framework; that is, facilitators who are key to the initiative’s implementation, such as those who develop and oversee the program, and staff and provider recipients who are trained and involved in the implementation process, such as day-to-day activities. The program coordinator also provided site documents for review. I reviewed site documents (e.g., grant application,

28 _{Site B and C had a PrEP Coordinator. As site A did not have a designated PrEP coordinator, a mid-} level staff member provided a list of potential participants.

workplans, operating manuals, and meeting minutes) for an in-depth understanding of each site’s implementation strategies.

I conducted baseline interviews between August 2017 and January 2018, and follow-up interviews between May and August 2018. I selected this strategy (two rounds of interviews, baseline and follow-up) in order to examine implementation throughout the program lifespan, from the early stages through when the program was more developed. Due to staff turnover and availability, not every participant interviewed at baseline was also interviewed at follow-up. Follow-up interviews included both baseline participants as well as newly-identified participants. I stopped interviews when saturation of themes was reached. Across sites, I conducted 18 baseline interviews at baseline and 15 follow- up interviews, with a total of 24 participants.30

I conducted one-on-one interviews either in-person or over the phone to allow for flexibility in participation. Interview length ranged between 25-68 minutes (mean length: 34 minutes). I asked participants to not include any identifying information in their answers. I gave participants a business card with my contact information (or an e-

business card if the interview was conducted over the phone). I audio-recorded interviews using two digital voice recorders.

I transcribed audio recordings verbatim using Express Scribe transcription software. I stored recordings and interview transcripts in a password-protected folder on my computer. I struck any identifying information, such as names, that was accidentally included in the audio recording from the transcription. I assigned each participant and

their corresponding transcription a unique identifier number; no identifying information was included. I kept a master electronic file that linked participants’ names with their unique identifier number in a separate password-protected file.

I analyzed data at the case (clinic) level as each setting was unique in its

implementation activities and program goals. I conducted data analysis using NVivo 11 qualitative data analysis software, guided by Ritchie and Lewis’ Framework Method, (162). The Framework Method, widely used in health research, is similar to thematic or content analysis in that it identifies commonalities and differences in data, examines relationships between data, and develops explanatory conclusions (163). This method allows data to be analyzed deductively, through pre-selected codes (e.g., implementation barriers and implementation facilitators) and also inductively, by leaving space for unexpected aspects or themes that may arise (163). I completed qualitative data analysis according to the five steps of the Framework Method, described below. Though five steps are listed, this analysis process is not linear, but rather iterative between data collection, analysis, and codebook development (163). A research assistant (RA) served as a second coder.

1) Familiarization with the interview: After I transcribed the audio recordings

verbatim, I compared the transcriptions to the recordings simultaneously to ensure that the transcriptions were completed accurately. After transcription was

completed, I wrote a brief memo remarking on initial impressions and themes.

2) Codebook development: The RA and I reviewed the first four transcriptions

discussion and agreed upon a set of corresponding codes.

3) Codebook application: Upon agreement of the codebook in step 2, we

separately applied the codebook to the remaining transcripts. We met weekly to discuss the coding process and ensure consistency. If new themes arose upon reading additional transcripts, we discussed these themes. If agreed upon, we added themes to the codebook and applied the new themes to the previously coded transcripts. The codebook was not considered finalized until the final transcript was coded.

4) Data charting: After all interviews were coded, I produced a summary of data

in a matrixed output across rows (interviews) and columns (codes) using NVivo’s “framework matrix” tool. This matrix provided a structure to systematically examine the data by interview and code.

5) Data interpretation: We explored data through discussion and completion of

analytic memos. We reflected upon impressions of the data and thoughts about the analysis process through the analytic memos. These analytic memos also provided a trail of the research process.