Examining Recruitment Strategies in the Enrollment Cascade of Youth Living With HIV: Descriptive Findings From a Nationwide Web-Based Adherence Protocol
Bibliographic record
Abstract
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.
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 | 0.041 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".