Is expected substance type associated with timing of drug checking service utilization?: A cross-sectional study
Bibliographic record
Abstract
Abstract Background Drug checking is a harm reduction intervention aiming to reduce substance use-related risks by improving drug user knowledge of the composition of unregulated drugs. With increasing fears of fentanyl adulteration in unregulated drugs, this study sought to examine whether the expected type of drug checked (stimulant vs. opioid) was associated with timing of drug checking service utilization (pre-consumption vs. post-consumption). Methods Data were derived from drug checking sites in British Columbia between October 31, 2017 and December 31, 2019. Pearson’s Chi-square test was used to examine the relationship between expected sample type (stimulant vs. opioid) and timing of service utilization. Odds ratios (OR) were calculated to assess the strength of this relationship. The Mantel–Haenszel (MH) test was used to adjust for service location. Results A total of 3561 unique stimulant and opioid samples were eligible for inclusion, including 691 (19.40%) stimulant samples; and 2222 (62.40%) samples that were tested pre-consumption. Results indicated a positive association between testing stimulant samples and testing pre-consumption (OR = 1.45; 95% CI 1.21–1.73). Regions outside of the epicenter of the province’s drug scene showed a stronger association with testing pre-consumption (ORMH = 2.33; 95% CI 1.51–3.56) than inside the epicenter (ORMH = 1.33; 95% CI 1.09–1.63). Conclusion Stimulant samples were more likely to be checked pre-consumption as compared with opioid samples, and stimulant samples were more likely to be tested pre-consumption in regions outside the epicenter of the province’s drug scene. This pattern may reflect a concern for fentanyl-adulterated stimulant 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".