Does size really matter? A sensitivity analysis of number of seeds in a respondent-driven sampling study of gay, bisexual and other men who have sex with men in Vancouver, Canada
Bibliographic record
Abstract
Background: Respondent-driven sampling (RDS) is an increasingly used peer chain-recruitment method to sample “hard-to-reach” populations for whom there are no reliable sampling frames. Implementation success of RDS varies; one potential negative factor being the number of seeds used. Methods: We conducted a sensitivity analysis on estimates produced using data from an RDS study of gay, bisexual and other men who have sex with men (GBMSM) aged ≥16 years living in Vancouver, Canada. Participants completed a questionnaire on demographics, sexual behavior and substance use. For analysis, we used increasing seed exclusion criteria, starting with all participants and subsequently removing unproductive seeds, chains of ≤1 recruitment waves, and chains of ≤2 recruitment waves. We calculated estimates for three different outcomes (HIV serostatus, condomless anal intercourse with HIV discordant/unknown status partner, and injecting drugs) using three different RDS weighting procedures: RDS-I, RDS-II, and RDS-SS. We also assessed seed dependence with bottleneck analyses and convergence plots. Statistical differences between RDS estimators were assessed through simulation analysis. Results: Overall, 719 participants were recruited, which included 119 seeds and a maximum of 16 recruitment waves (mean chain length = 1.7). The sample of >0 recruitment waves removed unproductive seeds (n = 50/119, 42.0%), resulting in 69 chains (mean length = 3.0). The sample of >1 recruitment waves removed 125 seeds or recruits (17.4% of overall sample), resulting in 37 chains (mean length = 4.8). The final sample of >2 recruitment waves removed a further 182 seeds or recruits (25.3% of overall sample), resulting in 25 chains (mean length = 6.1). Convergence plots and bottleneck analyses of condomless anal intercourse with HIV discordant/unknown status partner and injecting drugs outcomes were satisfactory. For these two outcomes, regardless of seed exclusion criteria used, the crude proportions fell within 95% confidence intervals of all RDS-weighted estimates. Significant differences between the three RDS estimators were not observed. Conclusions: Within a sample of GBMSM in Vancouver, Canada, this RDS study suggests that when equilibrium and homophily are met, although potentially costly and time consuming, analysis is not negatively affected by large numbers of unproductive or lowly productive seeds.
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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 | 0.391 | 0.495 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".