Web-Based Respondent-Driven Sampling to Assess Biobehavioral Factors Among Men Who Have Sex With Men in Thailand: Cross-Sectional Study
Bibliographic record
Abstract
Background: Respondent-driven sampling (RDS) is the current standard for sampling key populations at risk for HIV infections but is usually limited to local implementation in single towns or cities. Web-based sampling eliminates this spatial constraint but often relies on self-selected convenience samples. We piloted a web-based RDS survey with biomarker collection among men who have sex with men (MSM) in Thailand. Objective: This study aimed to evaluate and demonstrate the feasibility of implementing a web-based RDS survey as a routine surveillance system in Thailand. The goal was to enhance surveillance efforts targeting hard-to-reach populations in the country. Methods: We developed a website to fully function like a conventional RDS survey office, including coupon verification, eligibility screening, consenting, interviewing (self-administered), peer recruitment training, coupon issuance, compensation, and recruitment tracking. All functions were automated; data managers monitored recruitment, data collection, and payment and could be contacted by recruits as needed. Eligible participants were male, older than 15 years, resided in Thailand, and had anal sex with a man in the past 6 months. Recruits who resided in Bangkok were additionally invited to physically attend a participating health clinic of their choice for an HIV-related blood draw. Data were weighted to account for the complex sampling design. Results: The survey was implemented from February to June 2022; seeds (21 at start, 14 added later) were identified mostly through targeted web-based banner ads; coupon uptake was 45.1%. Of 2578 candidate recruits screened for eligibility, 2151 (83.4%) were eligible and 2142 (83.1%) enrolled. Almost all (2067/2578, 80.2%) completed the questionnaire; however, 318 survey records were removed from analysis as fraudulent enrollments. The final sample size was 1749, the maximum number of waves achieved was 191, and sampling covered all 6 geographic regions and 75 of 77 (97.4%) provinces; convergence was reached for several salient variables. The mean age was 20.5 (SD 4.0) years, and most (69.8%) had never tested for HIV before, with fear of stigma as the biggest reason (97.1%) for not having tested. Most (76.9%) had visited gay-focused physical venues several times a week. A condom was used in 97.6% of the last sex acts, 11.0% had purchased sex from other men (past 12 mo), 4.5% had sold sex to men (past 12 mo), and 95.3% had 3+ male sex partners (last 3 mo). No participant in Bangkok presented for a blood draw. Conclusions: We successfully conducted a web-based RDS survey among MSM in Thailand, covering nearly the entire country, although, as in physical RDS surveys, sampling was dominated by younger MSM. The survey also failed to collect biomarkers in Bangkok. Public health interventions should aim at increasing testing and addressing (the perception of) stigma.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".