Development of a Mobile App to Increase the Uptake of HIV Pre-exposure Prophylaxis Among Latino Sexual Minority Men: Qualitative Needs Assessment
Notice bibliographique
Résumé
BACKGROUND: HIV disproportionally impacts Latino sexual minority men (SMM). Uptake of pre-exposure prophylaxis (PrEP), an effective biomedical intervention to prevent HIV, is low in this group compared with White SMM. Mobile health technology represents an innovative strategy to increase PrEP uptake among Latino SMM. OBJECTIVE: We aimed to describe the qualitative process leading to the development of SaludFindr, a comprehensive HIV prevention mobile app aiming to increase PrEP uptake, HIV testing, and condom use by Latino SMM. METHODS: We conducted 13 in-depth interviews with Latino SMM living in the Atlanta area to explore their main barriers and facilitators to PrEP uptake and to analyze their opinions of potential SaludFindr app functionalities. To explore potential app functions, we used HealthMindr, an existing HIV prevention app, as a template and added new proposed features intended to address the specific community needs. RESULTS: We identified general PrEP uptake barriers that, although common among non-Latino groups, had added complexities such as the influence of religion and family on stigma. Low perceived PrEP eligibility, intersectional stigma, lack of insurance, cost concerns, and misconceptions about PrEP side effects were described as general barriers. We also identified Latino-specific barriers that predominantly hinder access to existing services, including a scarcity of PrEP clinics that are prepared to provide culturally concordant services, limited availability of Spanish language information related to PrEP access, distrust of peers as credible sources of information, perceived ineligibility for low-cost services owing to undocumented status, fear of immigration authorities, and competing work obligations that prevent PrEP clinic attendance. Health care providers represented a trusted source of information, and 3 provider characteristics were identified as PrEP facilitators: familiarity with prescribing PrEP; being Latino; and being part of lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQIA+) group or ally. The proposed app was very well accepted, with a particularly high interest in features that facilitate PrEP access, including a tailored list of clinics that meet the community needs and a private platform to seek PrEP information. Spanish language availability and free or low-cost PrEP care represented the 2 main clinic criteria that would facilitate PrEP uptake. Latino representation in clinic staff and providers; clinic perception as a safe space for undocumented patients; and LGBTQIA+ representation was listed as additional criteria. Only 8 of 47 clinics listed on the Centers for Diseases Control and Prevention PrEP locator website for the Atlanta area fulfilled at least 2 main criteria. CONCLUSIONS: This study provides further evidence of the substantial PrEP uptake barriers that Latino SMM face; exposes the urgent need to increase the number of accessible PrEP-providing clinics for Latino SMM; and proposes an innovative, community-driven, and mobile technology-based tool as a future intervention to overcome some of these barriers.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».