People who use drugs’ prioritization of regulation amid decriminalization reforms in British Columbia, Canada: A qualitative study
Bibliographic record
Abstract
BACKGROUND: North America and the province of British Columbia (BC), Canada, is experiencing an unprecedented number of overdose deaths. In BC, overdose has become the leading cause of death for people between the ages of 10-59 years old. In January 2023, BC decriminalized personal possession of a number of illegal substances with one aim being to address overdose deaths through stigma reduction and promoting access to substance use services. METHODS: We conducted a qualitative study to understand people who use drugs' (PWUD) perceptions of the new decriminalization policy, immediately prior to its' implementation (October-December 2022). To contextualize decriminalization within broader drug policy, we also asked PWUD what they perceived as the priority issues drug policy ought to address and the necessary solutions. Our final sample included 38 participants who used illegal drugs in the past month. RESULTS: We identified four themes: 1) The illicit drug supply as the main driver of drug toxicity deaths 2) Concerns about the impact of decriminalization on drug toxicity deaths 3) Views towards decriminalization as a policy response in the context of the drug toxicity crisis 4) Regulation as a symbol of hope for reducing drug toxicity deaths. CONCLUSION: From our data it became clear that many anticipated that decriminalization would have minimal or no impact on the overdose crisis. Regulation was perceived as the necessary policy approach for effectively and candidly addressing the drivers of the ongoing overdose crisis. These findings are important as jurisdictions consider different approaches to moving away from prohibition-based drug policy.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.032 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| 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".