Combining respondent-driven sampling with a community-based participatory action study of people who smoke drugs in two cities in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: The smoking of illicit drugs presents a serious social and economic burden in Canada. People who smoke drugs (PWSD) are at increased risk of contracting multiple infections through risky drug practices. Peer-led harm reduction activities, and the resulting social networks that form around them, can potentially minimize the dangers associated with the smoking illicit drugs. GOAL: The goals of this study were to pilot test the combined approaches of respondent driven sampling with community based participatory action research in these settings and compare the attributes and social networks of PWSD in two British Columbia cities with different harm reduction programs. METHODS: Using community-based participatory action research (CBPAR) and respondent-driven sampling (RDS), individuals with lived drug experiences were employed from communities in Abbotsford and Vancouver as peer researchers to interview ten contacts from their social networks. Contacts completed a questionnaire about their harm reduction behaviours and interactions. RESULTS: We found that PWSD residing in Abbotsford were more likely to report engaging in harm-promoting behaviours, such as sharing, reusing, or borrowing crack pipes. However, PWSD in the Downtown East side Community of Vancouver were more likely to report engaging in harm-reducing activities, such as being trained in naloxone use and CPR. We found no differences in network sizes between the two communities, despite the population differences and harm reduction programs CONCLUSION: The high participation rates and interactions between researchers, and peer researchers enriched the study implementation and successfully informed our results. The fact that there were no differences in network size suggests that people have similar support in Vancouver as in Abbotsford, and that drug use practices differ mainly due to availability of harm reduction programming and resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".