Comparing Social Media and In-Person Recruitment: Lessons Learned From Recruiting Substance-Using, Sexual and Gender Minority Adolescents and Young Adults for a Randomized Control Trial
Bibliographic record
Abstract
BACKGROUND: Recruiting large samples of diverse sexual and gender minority adolescent and young adults (AYAs) into HIV intervention research is critical to the development and later dissemination of interventions that address the risk factors for HIV transmission among substance-using, sexual and gender minority AYAs. OBJECTIVE: This paper aimed to describe the characteristics of the samples recruited via social media and in-person methods and makes recommendations for strategies to recruit substance-using, sexual and gender minority AYAs, a hardly reached population that is a priority for HIV prevention research. METHODS: Using data from a randomized control trial of an HIV and substance use intervention with sexual and gender minority AYAs, aged 15 to 29 years in southeastern Michigan (n=414), we examined demographic and behavioral characteristics associated with successful recruitment from a range of virtual and physical venues. RESULTS: We found that paid advertisements on Facebook, Instagram, and Grindr offered the largest quantity of eligible participants willing to enroll in the trial. Instagram offered the largest proportion of transgender masculine participants, and Grindr offered the largest proportion of Black/African American individuals. Although we attempted venue-based recruitment at clubs, bars, community centers, and AIDS service organizations, we found it to be unsuccessful for this specific hardly reached population. Social media and geobased dating applications offered the largest pool of eligible participants. CONCLUSIONS: Understanding factors associated with successful recruitment has the potential to inform effective and efficient strategies for HIV prevention research with substance-using, sexual and gender AYAs. TRIAL REGISTRATION: ClinicalTrials.gov NCT02945436; https://clinicaltrials.gov/ct2/show/NCT02945436. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.9414.
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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 | 0.443 | 0.567 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".