Adaptation of a Theory-Based, Clinic-Affiliated Smartphone App to Improve HIV Testing and Pre-exposure Prophylaxis Uptake Among Gay, Bisexual, and Other Men Who Have Sex With Men in Malaysia
Bibliographic record
Abstract
Background In Malaysia, HIV disproportionately affects gay, bisexual, and other men who have sex with men (GBMSM). Homosexuality and substance use are criminalized in Malaysia, making GBMSM bear multilevels of social stigma and discrimination, including in health care. Mobile health (mHealth), particularly smartphone apps, is a promising and cost-effective strategy for reaching stigmatized and hard-to-reach populations like GBMSM and linking them to HIV prevention services (eg, HIV testing and pre-exposure prophylaxis [PrEP]), particularly in the context of COVID-19. Objective This study aimed to adapt the HealthMindr app (Emory University), which was developed with GBMSM in the United States, to improve HIV testing and PrEP uptake for GBMSM in Malaysia. Methods We conducted online focus group discussions (FGDs) between August and September 2021 with 20 GBMSM and 16 community stakeholders (eg, doctors, nurses, pharmacists, and nongovernmental organization staff). Participants were asked questions regarding their preferences for functions and features in mHealth apps among GBMSM and suggestions for adapting the HealthMindr app to the Malaysian context. Each session was digitally recorded and transcribed. Transcripts were inductively coded using Dedoose software (University of California, Los Angeles) and analyzed to identify and interpret emerging themes. Results The FGDs with GBMSM revealed preferences for interfacing with apps to access HIV testing, PrEP, and counseling services. Stakeholders showed strong interest in using the app-based platform to deliver integrated care (eg, HIV and mental health). The key themes mostly focused on adaptation and refinement for the Malaysian context and were related to cultural and stylistic preferences (design and user interface), engagement strategies (reward systems, marketing campaigns, and reminders), and recommendations for new functions (enhanced communication options via chat and discussion forums) in a one-stop hub for all HIV prevention needs (HIV self-testing, PrEP, and postexposure prophylaxis) that minimize privacy and confidentiality risks. Conclusions Our data suggest that a tailored HIV-prevention app would be acceptable for GBMSM in Malaysia. The findings provided detailed recommendations for the successful adaptation and refinement of the existing platform for optimal use in the Malaysian context. Conflicts of Interest None declared.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".