Adaptation of a Theory-Based Mobile App to Improve Access to HIV Prevention Services for Transgender Women in Malaysia: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Globally, transgender women have been disproportionately affected by the HIV epidemic, including in Malaysia, where an estimated 11% of transgender women are living with HIV. Available interventions designed specifically to meet transgender women's needs for HIV prevention are limited. Mobile health, particularly smartphone mobile apps, is an innovative and cost-effective strategy for reaching transgender women and delivering interventions to reduce HIV vulnerability. OBJECTIVE: This study aims to adapt a theory-based mobile health HIV prevention smartphone app, HealthMindr, to meet the unique needs of transgender women in Malaysia. We conducted theater testing of the HealthMindr app with transgender women and key stakeholders and explored barriers to transgender women's uptake of HIV pre-exposure prophylaxis (PrEP). METHODS: From February to April 2022, a total of 6 focus group (FG) sessions were conducted with 29 participants: 4 FG sessions with transgender women (n=18, 62%) and 2 FG sessions with stakeholders (n=11, 38%) providing HIV prevention services to transgender women in Malaysia. Barriers to PrEP uptake and gender-affirming care services among transgender women in Malaysia were explored. Participants were then introduced to the HealthMindr app and provided a comprehensive tour of the app's features and functions. Participants provided feedback on the app and on how existing features should be adapted to meet the needs of transgender women, as well as any features that should be removed or added. Each FG was digitally recorded and transcribed. Transcripts were coded inductively using Dedoose software (version 9.0.54; SocioCultural Research Consultants, LLC) and analyzed to identify and interpret emerging themes. RESULTS: Six subthemes related to PrEP barriers were found: stigma and discrimination, limited PrEP knowledge, high PrEP cost, accessibility concerns, alternative prevention methods, and perceived adverse effects. Participants suggested several recommendations regarding the attributes and app features that would be the most useful for transgender women in Malaysia. Adaptation and refinement of the app were related to the attributes of the app (user interface, security, customizable colors, themes, and avatars), feedback, and requests for additional mobile app functional (appointment booking, e-consultation, e-pharmacy, medicine tracker, mood tracker, resources, and service site locator) and communication (peer support group, live chat, and discussion forum) features. CONCLUSIONS: The results reveal that multifaceted barriers hinder PrEP uptake and use among transgender women in Malaysia. The findings also provide detailed recommendations for successfully adapting the HealthMindr app to the context of Malaysian transgender women, with a potential solution for delivering tailored HIV prevention, including PrEP, and increasing accessibility to gender-affirming care services.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".