The effect of recreational cannabis legalization and commercialization on substance use, mental health, and injury: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of recreational cannabis legalization (RCL) and/or recreational cannabis commercialization (RCC) on emergency department (ED) visits, hospitalizations, and deaths due to substance use, injury, and mental health among those aged 11 years and older. METHODS: A systematic review of six electronic databases up to February 1, 2023. Original, peer-reviewed articles with interrupted time series or before and after designs were included. Four independent reviewers screened articles and assessed risk of bias. Outcomes with 'critical' risk of bias were excluded. Protocol registered on PROSPERO (# CRD42021265183). RESULTS: After screening and risk of bias assessment, 29 studies were included which examined ED visits or hospitalizations for cannabis use or alcohol (N = 10), opioid mortality (N = 3), motor vehicle fatalities or injury (N = 11), and intentional injury/mental health (N = 5). Rates or number of cannabis-related hospitalizations increased after RCL in Canada and the USA. Immediate increases in rates of cannabis-related ED visits were found after both RCL and RCC in Canada. Rates of traffic fatalities increased after RCL and RCC in certain jurisdictions in the USA. CONCLUSIONS: RCL was associated with increased rates of cannabis-related hospitalizations. RCL and/or RCC was associated with increased rates of cannabis-related ED visits, consistently shown across sex and age groups. The effect on fatal motor vehicle incidents was mixed, with observed increases found after RCL and/or RCC. The effect of RCL or RCC on opioids, alcohol, intentional injury, and mental health is not clear. These results inform population health initiatives and international jurisdictions considering RCL implementation.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".