Factors associated with drug checking service utilization among people who use drugs in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: The United States and Canada are amidst an opioid overdose crisis, with the Canadian province of British Columbia (BC) among the hardest hit. In response, drug checking services (DCS) have been introduced in this setting as a novel pilot harm reduction intervention though little is known about usage rates. Therefore, we sought to identify factors associated with drug checking uptake among people who use drugs (PWUD) in Vancouver, BC. METHODS: Data were derived from three ongoing prospective cohort studies of PWUD in Vancouver between June and November 2018. Multivariable logistic regression was used to determine factors associated with self-reported DCS utilization in the past 6 months among participants at high risk of fentanyl exposure (i.e., those self-reporting illicit opioid use or testing positive for fentanyl via urine drug screen). RESULTS: Among 828 eligible participants, including 451 (55%) males, 176 (21%) reported recent use of DCS. In multivariable analyses, factors significantly associated with DCS utilization included: homelessness (Adjusted Odds Ratio [AOR] 1.47; 95% Confidence Interval [CI] 1.01-2.13) and involvement in drug dealing (AOR 1.59; 95% CI 1.05-2.39). CONCLUSIONS: In our sample of PWUD, uptake of DCS was low, although those who were homeless, a sub-population known to be at a heightened risk of overdose, were more likely to use the services. Those involved in drug dealing were also more likely to use the services, which may imply potential for improving drug market safety. Further evaluation of drug checking is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".