Assessing the public health impacts of legalizing recreational cannabis use: the US experience
Bibliographic record
Abstract
The sale of cannabis for adult recreational use has been made legal in nine US states since 2012, and nationally in Uruguay in 2013 and Canada in 2018. We review US research on the effects of legalization on cannabis use among adults and adolescents and on cannabis-related harms; the impact of legalizing adult recreational use on cannabis price, availability, potency and use; and regulatory policies that may increase or limit adverse effects of legalization. The legalization of recreational cannabis use in the US has substantially reduced the price of cannabis, increased its potency, and made cannabis more available to adult users. It appears to have increased the frequency of cannabis use among adults, but not so far among youth. It has also increased emergency department attendances and hospitalizations for some cannabis-related harms. The relatively modest effects on cannabis use to date probably reflect restrictions on the number and locations of retail cannabis outlets and the constraints on commercialization under a continued federal prohibition of cannabis. Future evaluations of legalization should monitor: cannabis sales volumes, prices and content of tetrahydrocannabinol; prevalence and frequency of cannabis use among adolescents and adults in household and high school surveys; car crash fatalities and injuries involving drivers who are cannabis-impaired; emergency department presentations related to cannabis; the demand for treatment of cannabis use disorders; and the prevalence of regular cannabis use among vulnerable young people in mental health services, schools and the criminal justice system. Governments that propose to legalize and regulate cannabis use need to fund research to monitor the impacts of these policy changes on public health, and take advantage of this research to develop ways of regulating can-nabis use that minimize adverse effects on public health.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".