Using respondent-driven sampling with ‘hard to reach’ marginalised young people: problems with slow recruitment and small network size
Bibliographic record
Abstract
This paper documents an experience of using respondent-driven sampling (RDS) to recruit socially marginalised young people in Sydney, Australia. Respondents were young people aged 16–24 years who were current illicit drug users and who reported at least one feature of social marginalisation (e.g. recent homelessness or juvenile detention). Four seeds initiated the sampling and 61 respondents were recruited until the sampling was closed due to slow progress at week nine. The paper examines: (1) the overall success of RDS and compares this with similar RDS studies; and (2) the sufficiency of network ties among respondents. The analyses suggest that RDS was generally successful in that, despite its small size, the sample achieved adequately long recruitment chains and variables converged to equilibrium. Nevertheless, recruitment was much slower than comparable studies. This could be due to the study population having reduced willingness to participate, a high proportion of respondents who did not fit the selection criteria, and small and disparate networks. Using RDS with marginalised youth may require generous resourcing to allow large incentives to increase willingness, and a lengthy recruitment period. Moreover, the small networks suggest that researchers should start the sampling with a large number of 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 | 0.256 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".