Examining Recruitment Strategies in the Enrollment Cascade of Youth Living With HIV: Descriptive Findings From a Nationwide Web-Based Adherence Protocol
Notice bibliographique
Résumé
BACKGROUND: Digital strategies and broadened eligibility criteria may optimize the enrollment of youth living with HIV in mobile health adaptive interventions. Prior research suggests that digital recruitment strategies are more efficient than traditional methods for overcoming enrollment challenges of youth living with HIV in the United States. OBJECTIVE: This study highlights the challenges and strategies that explain screening and enrollment milestones in a national web-based adherence protocol for youth living with HIV. METHODS: Baseline data from a national web-based HIV adherence protocol for youth living with HIV, collected from July 2018 to February 2021, were analyzed. A centralized recruitment procedure was developed, which used web-based recruitment via Online Master Screener; paid targeted advertisements on social media platforms (eg, Facebook and Reddit) and geosocial networking dating apps (eg, Grindr and Jack'd); and site and provider referrals from Subject Recruitment Venues and other AIDS service organizations, website referrals, and text-in recruitment. RESULTS: A total of 3 distinct cohorts of youth living with HIV were identified, marked by changes in recruitment strategies. Overall, 3270 individuals consented to screening, 2721 completed screening, 581 were eligible, and 83 completed enrollment. We examined sociodemographic and behavioral differences in completing milestones from eligibility to full enrollment (ie, submitting antiretroviral therapy and viral load data and completing the baseline web-based survey). Those with the most recent viral load tests >6 months ago were half as likely to enroll (odds ratio 0.45, 95% CI 0.21-0.94). Moreover, eligible participants with self-reported antiretroviral therapy adherence (SRA) between 50% and 80% were statistically significant (P<.001 to P=.03) and more likely to enroll than those with SRA >80%. CONCLUSIONS: The findings add to our knowledge on the use of digital technologies for youth living with HIV before and during the COVID-19 pandemic and provide insight into the impact of expanding eligibility criteria on enrollment. As the COVID-19 pandemic continues and the use of and engagement with social media and dating apps among youth living with HIV changes, these platforms should continue to be investigated as potential recruitment tools. Using a wide variety of recruitment strategies such as using social media and dating apps as well as provider referral mechanisms, increasing compensation amounts, and including SRA in enrollment criteria should continue to be studied with respect to their ability to successfully recruit and enroll eligible participants. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/11183.
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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 | 0,041 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».