Effect of Nonmedical Cannabis Legalization and Exposure to Retail Stores on Cannabis Harms
Bibliographic record
Abstract
BACKGROUND: In 2018, Canada became the second country to legalize nonmedical cannabis and the first to allow a commercial retail market. Limiting the density of stores selling other legal substances is associated with reductions in use and harms; however, similar associations for cannabis are not well established. OBJECTIVE: To examine the association between exposure to cannabis retail stores and cannabis-related harms. DESIGN: Population-based natural experiment examining cannabis-attributable emergency department (ED) visits between 2017 and 2022. SETTING: Ontario, Canada. PARTICIPANTS: 10 574 neighborhoods containing 6 140 595 persons aged 15 to 105 years. MEASUREMENTS: The opening of all cannabis stores in Ontario was tracked to identify neighborhoods that became exposed (cannabis store within 1000 m) over time. Absolute and relative changes in rates of cannabis-attributable ED visits were compared in neighborhoods after they became exposed with matched neighborhoods that remained unexposed. RESULTS: < 0.001) compared with unexposed neighborhoods, which was equivalent to a 12% (CI, 6% to 19%) relative increase in the monthly rate of visits. LIMITATION: Findings may be influenced by unmeasured confounding between exposed and unexposed neighborhoods. CONCLUSION: Findings suggest that prohibiting stores in certain areas, limiting store density, or placing restrictions on the overall number of stores may offer public health benefits in countries pursuing legalization. PRIMARY FUNDING SOURCE: Canadian Institutes of Health Research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".