Cannabis-related Hospitalizations Among Youth in Canada Before and After Cannabis Legalization
Bibliographic record
Abstract
OBJECTIVES: We investigated trends in cannabis-related hospitalizations among youth before and after legalization of recreational cannabis in Canada on October 17, 2018. METHODS: We computed rates of cannabis-related hospitalizations and the proportion of substance-related hospitalizations involving cannabis by age and sex in the months before and after legalization in Quebec, Canada. RESULTS: In boys aged 10 to 14 years, cannabis-related hospitalization rates increased from 5.2 per 100,000 one year before legalization [95% confidence interval (CI) 2.9-9.3] to 9.5 per 100,000 after legalization (95% CI 6.2-14.6), although the increase was not statistically significant. Cannabis was reported in 39.3% of substance-related hospitalizations in boys aged 10 to 14 years before legalization, compared with 70.0% after legalization, representing a difference of 30.7% (95% CI 2.8-58.6). There was no increase in cannabis-related hospitalizations among girls or boys aged 15 to 19 years. CONCLUSIONS: This study suggests that cannabis legalization in Canada did not increase the risk of short-term cannabis-related hospitalization among girls and older boys. However, legalization may have contributed to an increase in the risk in boys under 15 years. Further data are needed to clarify trends after legalization for younger adolescents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".