Preferences for and Experiences of an HIV-Prevention Mobile App Designed for Transmasculine People: Pilot Feasibility Trial and Qualitative Investigation
Bibliographic record
Abstract
BACKGROUND: Transmasculine people are at risk for HIV; yet few HIV prevention interventions have been developed for this population. We adapted an existing HIV prevention smartphone app for cisgender men who have sex with men to meet the sexual health needs of transmasculine people. OBJECTIVE: This study aims to assess the acceptability of the adapted app, Transpire, among transmasculine people living in Atlanta, Georgia, and Washington, DC, via in-depth interviews of participants in a pilot feasibility trial. METHODS: Participants used the Transpire app for 3 months as part of a pilot study of the app. Eligible participants were aged 18-34 years. There were no eligibility criteria with respect to race and ethnicity, and most participants were non-Hispanic White. At the end of the follow-up, participants were invited to participate in web-based in-depth interviews to discuss their experiences using the app and feedback on design and content. Interviews were transcribed and coded using a constant comparative approach. Three main themes were identified: sexual behavior, app experiences and feedback, and pre-exposure prophylaxis. RESULTS: Overall, participants found the app acceptable and thought that it would be a useful tool for themselves and their peers. Participants reported appreciating having a comprehensive information source available to them on their phones and reported learning more about HIV, sexually transmitted infections, and pre-exposure prophylaxis via the app. They also reported appreciating the inclusive language that was used throughout the app. Although the app included some resources on mental health and substance use, participants reported that they would have appreciated more resources and information in these areas as well as more comprehensive information about other health concerns, including hormone therapy. Representative quotes are presented for each of the identified themes. CONCLUSIONS: There is a desire to have greater access to reliable sexual health information among transmasculine people. Mobile apps like Transpire are an acceptable intervention to increase access to this information and other resources. More evidence is needed, however, from more racially and ethnically diverse samples of transmasculine people.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".