Cannabis legalization and cannabis use, daily cannabis use and cannabis-related problems among adults in Ontario, Canada (2001–2019)
Bibliographic record
Abstract
BACKGROUND: In the context of cannabis legalization in Canada, we examined the effects on cannabis patterns of consumption, including cannabis use, daily cannabis use and cannabis-related problems. In addition, we examined differential effects of cannabis legalization by age and sex. METHODS: A pre-post design was operationalized by combining 19 iterations of the Centre for Addiction and Mental Health (CAMH) Monitor Surveys (N = 52,260; 2001-2019): repeated, population-based, cross-sectional surveys of adults in Ontario. Participants provided self-reports of cannabis use (past 12 months), daily cannabis use (past 12 months) and cannabis-related problems though telephone interviews. The effects of cannabis legalization on cannabis patterns of consumption were examined using logistic regression analyses, with testing of two-way interactions to determine differential effects by age and sex. RESULTS: Cannabis use prevalence increased from 11 % to 26 % (p < 0.0001), daily cannabis use prevalence increased from 1 % to 6 % (p < 0.0001) and cannabis-related problems prevalence increased from 6 % to 14 % (p < 0.0001) between 2001 and 2019. Cannabis legalization was associated with an increased likelihood of cannabis use (OR, 95 % CI: 1.62, 1.40-1.86), daily cannabis use (1.59, 1.21-2.07) and cannabis-related problems (1.53, 1.20-1.95). For cannabis-related problems, a significant two-way interaction was observed between cannabis legalization and age (p = 0.0001), suggesting differential effects among adults ≥55 years. CONCLUSIONS: Cannabis legalization was associated with an increased likelihood of cannabis use, daily cannabis use and cannabis-related problems. Given increases in these cannabis patterns of consumption, broader dissemination and uptake of targeted prevention tools is indicated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".