Adaptation of a Theory-Based Mobile App to Improve Access to HIV Prevention Services for Transgender Women in Malaysia: Focus Group Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».