Respondent-Driven Sampling With Youth Who Use Drugs: A Mixed Methods Assessment
Bibliographic record
Abstract
Respondent-driven sampling (RDS) has been widely used for recruiting hard-to-sample populations, particularly men who have sex with men and people who inject drugs from large urban centers. The aim of this article was to examine the feasibility of using RDS among nonmetropolitan youth who use drugs. Between May 2017 and June 2018, RDS was used to recruit youth who use drugs, ages 16–30, in three nonmetropolitan Canadian cities. All participants completed a 1-hr interviewer-administered survey. Youth received $25 for the interview, up to five coupons to recruit peers and $5 per coupon returned. Crude and RDS-weighted descriptive statistics were produced using RDS-II weights as were homophily (the tendency for people to be similar) and network size estimates. Statistically significant differences between seeds and recruits were identified using logistic regression. A subsample of recruits participated in qualitative interviews ( n = 38). Data from these interviews were inductively analyzed to identify barriers that could be used to explain the challenges with chain-referral recruitment among this population. In total, 449 youth were recruited. Due to unproductive chains, 57.2% ( n = 257) of the sample was comprised of seeds and 322 (72%) did not have a single coupon returned. Barriers to recruiting other youth included logistical challenges, fear of police, concerns about confidentiality, stigma of substance use, and poor financial incentive. Our study shows that RDS can be used to reach younger participants but also highlights the need for formative research and flexibility in recruitment to help mitigate unsuccessful RDS among nonmetropolitan youth who use drugs.
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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 | 0.108 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".