Empowering With PrEP (E-PrEP), a Peer-Led Social Media–Based Intervention to Facilitate HIV Preexposure Prophylaxis Adoption Among Young Black and Latinx Gay and Bisexual Men: Protocol for a Cluster Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Young black and Latinx, gay, bisexual, and other men who have sex with men (YBLGBM, aged 18-29 years) have among the highest rates of new HIV infections in the United States and are not consistently reached by existing prevention interventions. Preexposure prophylaxis (PrEP), an oral antiretroviral regimen taken daily by HIV-uninfected individuals to prevent HIV acquisition, is highly efficacious in reducing HIV acquisition and could help stop the HIV epidemic in YBLGBM. Use of social media (eg, Facebook, Twitter, online dating sites) is ubiquitous among young people, providing an efficient avenue to engage YBLGBM to facilitate PrEP adoption. OBJECTIVE: Our overall goal was to develop and pilot test a theoretically grounded, social media-based, peer-led intervention to increase PrEP uptake in YBLGBM. We used diffusion of innovation and information-motivation-behavioral skills frameworks to (1) identify potential factors associated with interest in and adoption of PrEP among YBLGBM; (2) develop Empowering with PrEP (E-PrEP), a social media-based, peer-led intervention to increase PrEP uptake in YBLGBM; and (3) pilot test the feasibility and acceptability of E-PrEP, and determine its preliminary efficacy for increasing adoption of PrEP by YBLGBM. We describe the development and protocol for E-PrEP. METHODS: Using a participatory research approach, we partnered with YBLGBM intervention development partners to develop a social media-based behavioral intervention to facilitate PrEP uptake, which involved an online messaging campaign disseminated by YBLGBM peer leaders to their existing online networks. We designed the 6-week campaign to provide education about PrEP, increase motivation to use PrEP, and facilitate access to PrEP. We then conducted a cluster-randomized trial of E-PrEP compared with an attention-matched general health control condition (E-Health) among YBLGBM aged 18 to 29 years to assess E-PrEP's feasibility, acceptability, preliminary efficacy for increasing self-reported intention to use PrEP, PrEP uptake, and impact on knowledge and attitudes about PrEP at 12-week follow-up (6 weeks after the end of the online campaign). RESULTS: From October 2016 to March 2017, we developed, pretested, and refined E-PrEP with 6 YBLGBM intervention development partners. From May to June 2017, we recruited, enrolled, and randomly assigned 10 peer leaders (n=5 for each condition). The 10 peer leaders then recruited and enrolled 152 participants from their existing online networks (range 3-33 per peer leader), during June and July 2017. Intervention follow-up was completed after 12 weeks, in November 2017, with analyses underway. CONCLUSIONS: We hypothesize that, compared with E-Health, participants randomly assigned to E-PrEP will be more likely to express intention to use PrEP and greater PrEP uptake, and will also show changes in potential mediators of PrEP uptake (knowledge, attitudes, stigma, and access). A Web-based biobehavioral intervention model such as E-PrEP could be rapidly scaled even with limited resources and have significant population-level impact. TRIAL REGISTRATION: ClinicalTrials.gov NCT03213366; https://clinicaltrials.gov/ct2/show/NCT03213366 (Archived by WebCite at http://www.webcitation.org/71onSdcXY). REGISTERED REPORT IDENTIFIER: RR1-10.2196/11375.
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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 | 0,021 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,008 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,085 | 0,009 |
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 ».