Effect of witnessing an overdose on the use of drug checking services among people who use illicit drugs in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: Since 2013, fentanyl-contaminated drugs have been driving North America's opioid-overdose epidemic. Drug checking, which enables people who use illicit drugs (PWUD) to test and receive feedback regarding the contents of their drugs, is being considered as a potential tool to address the toxic drug supply. While some PWUD witness overdoses, little is known about the impact of these experiences on subsequent risk reduction practices. OBJECTIVE: The purpose of this study was to examine the effect of witnessing an overdose on drug checking service use. METHODS: Data were derived from prospective cohorts of PWUD in Vancouver, Canada, a setting with a community-wide fentanyl overdose crisis, between June 1, 2018 and December 1, 2018. Multivariable logistic regression was used to estimate the effect of witnessing an overdose on drug checking service use. RESULTS: 1,426 participants were eligible for the study, including 530 females; 767 (53.8%) participants reported witnessing an overdose and 196 (13.7%) reported using drug checking services in the last 6 months. In multivariable analyses, after adjusting for a range of confounders including the use of fentanyl, witnessing an overdose was positively associated with drug checking service use (adjusted odds ratio = 2.32; 95% confidence interval: 1.57-3.49). CONCLUSION: Our findings suggest that witnessing an overdose may motivate PWUD to use drug checking services. Given that only a small proportion of PWUD in the study reported using drug checking services, our findings highlight the need to continue to scale-up a range of overdose prevention interventions.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".