Engaging Sexual and Gender Minority Youth in HIV Interventions Through Gay Dating Apps: Recruitment Protocol
Bibliographic record
Abstract
BACKGROUND: HIV continues to disproportionately impact sexual and gender minority youth (SGMY) in the United States. Public health efforts have increasingly focused on developing efficacious interventions to curb the spread of HIV among SGMY and help those living with HIV achieve and sustain viral suppression. However, recruiting and engaging SGMY in prevention and care interventions is challenging. OBJECTIVE: During the past decade, gay dating apps have quickly emerged as popular web-based spaces in which SGMY congregate. Although the recruitment of SGMY through these apps has been commonly reported, advertisement is the typical modality used, and direct recruitment approaches are not adequately described. This study aims to describe the process for developing a direct recruitment protocol for use in gay dating apps. METHODS: The Adolescent Medicine Trials Network Comprehensive Adolescent Research and Engagement Studies is a community-based research program consisting of 3 interrelated studies testing scalable behavioral interventions to improve HIV prevention and care engagement among youth aged 12-24 years in Los Angeles and New Orleans. To supplement our in-person recruitment approaches for Comprehensive Adolescent Research and Engagement Studies, the New Orleans site formed a gay dating app recruitment team. In April 2018, the team developed a loosely structured protocol that included study-specific profiles and sample language to guide initial recruitment efforts. Two self-identified Black, gay cisgender male field recruiters field-tested the protocol on the popular gay dating app Jack'd. During the field test, the recruitment team met weekly to discuss the recruiters' experiences and user reactions. For example, we learned the importance of addressing concerns about study legitimacy and identifying appropriate ways to describe the study. We iteratively incorporated these lessons learned into the final protocol and developed a training program and tracking procedures before moving to full-scale implementation at both sites. RESULTS: Adhering to this protocol yielded 162 enrollments in New Orleans (332 total enrollments across the two sites) throughout the recruitment period (April 2018 to August 2019). Most of these participants were sexual minority cisgender males (91%), and the remainder were identified as members of gender minority groups. We outlined step-by-step instructions on training staff, engaging users, and scheduling and tracking recruitment activities. CONCLUSIONS: This paper provides a practical guide for researchers and community-based providers to implement a gay dating app recruitment protocol. Our experience indicates that gay dating app recruitment is feasible and fruitful when the staff members are knowledgeable, flexible, honest, and respectful to the user. Perhaps the most salient lesson we learned in approaching gay dating app users is the importance of setting clear and transparent intentions without judgment. As gay dating apps continue to increase in popularity, researchers need to stay vigilant to changing formats and develop systematic approaches to harness their potential as invaluable recruitment strategies for SGMY. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/28864.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.126 | 0.039 |
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".