Minimum Legal Age of Nonmedical Cannabis Purchase Laws and Cannabis-Related Hospitalizations in Canada, 2015 to 2022
Bibliographic record
Abstract
Objectives. To determine whether the minimum legal age (MLA) for cannabis purchases is associated with reductions in cannabis-related hospitalizations in youths. Methods. We performed a population-based study examining all hospitalizations for cannabis use in Canada for individuals aged 15 to 44 years (n = 14.6 million in 2018) between January 1, 2015, and March 31, 2022. MLAs varied across Canada. We used a controlled interrupted time series design to compare changes in cannabis-related hospitalizations between individuals above and below the MLA. Results. There were 137 901 cannabis-related hospitalizations during the study. Prelegalization rates of hospitalizations were increasing by 2% per quarter for individuals above and below the MLA. After legalization, hospitalizations began declining by 2% per quarter in individuals below the MLA (rate ratio [RR] quarterly slope change = 0.96; 95% confidence interval [CI] = 0.95, 0.98) with no slope change for individuals above the MLA. The total effect, 3.5 years after legalization, was a 34% reduction (relative difference = 0.66; 95% CI = 0.49, 0.91; P = .011) in hospitalizations for those below relative to those above the MLA. Conclusions. Nonmedical cannabis legalization in Canada was associated with reductions in cannabis-related hospitalizations for youths below the MLA and with ongoing increases for individuals above the MLA. Public Health Implications. The results suggest that cannabis legalization may increase cannabis-related hospitalizations in adults but that MLAs may prevent such increases for at-risk young people in regions pursuing cannabis legalization. ( Am J Public Health. 2025;115(7):1166–1174. https://doi.org/10.2105/AJPH.2025.308090 )
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".