Decriminalization of drug possession in British Columbia and hospitalizations for opioid poisoning
Bibliographic record
Abstract
OBJECTIVES: In January of 2023, the provincial government of British Columbia, Canada, received federal approval to decriminalize the personal possession of certain illegal drugs. The policy had multiple aims, including a long-term goal of reducing drug-related overdoses by decreasing stigma associated with drug use and promoting health service and treatment engagement. In May of 2024, the policy was amended to recriminalize drug possession in public spaces. We evaluated the association between the implementation of British Columbia's drug decriminalization policy, including both the initial enactment and the May 2024 amendment, and opioid-related poisoning hospitalizations. STUDY DESIGN: We conducted interrupted time series analyses using quarterly data on opioid-related poisonings leading to hospitalization. METHODS: The study period spanned from the first quarter of 2016 to the third quarter of 2024, inclusively. Data were sourced from British Columbia and other Canadian provinces without decriminalization (excluding Quebec, Newfoundland and Labrador, and Prince Edward Island). Two intervention time points were assessed: January 31, 2023, marking the implementation of the initial decriminalization exemption, and May 7, 2024, when a substantial amendment to the exemption was enacted. Data were analyzed using generalized additive models. RESULTS: We found no association between the slope of opioid-related poisoning hospitalization rates associated with the original enactment of the decriminalization legislation, and also no associations with this indicator after the May 7th amendment either in level or slope. CONCLUSIONS: Our findings indicate that decriminalization was not associated with increases in opioid-related poisoning hospitalizations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".