Canada’s Cannabis Legalization with Strict Public Health Control
Bibliographic record
Abstract
Abstract: Aims: To describe the impact of the legalization of cannabis for recreational use under strict public health control in 2018 on the following outcomes: cannabis use and use patterns, attributable harm, economic considerations. Methodology: Narrative review based on government documents, surveys, and published literature. Results: The 12-month prevalence increased after legalization and has decreased during the COVID-19 pandemic. Little change in prevalence for adolescents. Persons with daily use remained stable. No rigorous studies on changes in attributable health harm, but some indication that harm, as measured in prevalence of cannabis use disorders, treatment rate, and attributable traffic injury remained stable. No data yet available for cancer. Cannabis attributable emergency visits increased, including among children (poisoning). Cannabis-related offences decreased as biggest public health gain. Economic predictions were not realized, and there is some pressure from cannabis industry to loosen public health regulations in order to increase use. Conclusions: Overall, while not achieving its main objectives of more youth protection and decreases in cannabis-attributable health harm, legalization with strict public health control resulted in less cannabis-related offences and up to now did not seem to increase cannabis-attributable disease burden.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".