Guidelines for public health and safety metrics to evaluate the potential harms and benefits of cannabis regulation in Canada
Bibliographic record
Abstract
ISSUES: Canada recently introduced a public health-based regulatory framework for non-medical cannabis. This review sought to identify a comprehensive set of indicators to evaluate the public health and safety impact of cannabis regulation in Canada, and to explore the ways in which these indicators may be expected to change in the era of legal non-medical cannabis. APPROACH: Five scientific databases were searched to compile a list of cannabis-related issues of interest to public health and safety. A set of indicators was developed based on topics and themes that emerged. Preliminary evidence from other jurisdictions in the USA and Canada that have legalised medical and/or non-medical cannabis (e.g. Colorado, Washington) was summarised for each indicator, wherever possible. KEY FINDINGS: In total, 28 indicators were identified under five broad themes: public safety; cannabis use trends; other substance use trends; cardiovascular and respiratory health; and mental health and cognition. Preliminary trends from other legalised jurisdictions reveal little consensus regarding the effect of cannabis legalisation on public health and safety harms and an emerging body of evidence to support potential benefits (e.g. reductions in opioid use and overdose). IMPLICATIONS: In addition to indicators of commonly discussed challenges (e.g. cannabis-related hospitalisations, cannabis-impaired driving), this review led to the recommendation of several indicators to monitor for possible public health and safety improvements. CONCLUSION: In preparing a comprehensive public health and safety monitoring and evaluation system for cannabis regulation, this review underscores the importance of not only measuring the possible risks but also the potential benefits.
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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 | 0.118 | 0.275 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.034 | 0.038 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".