Changes in Youth Cannabis Use After an Increase in Cannabis Minimum Legal Age in Quebec, Canada
Bibliographic record
Abstract
Importance: In January 2020, Quebec raised the minimum legal age (MLA) for cannabis from 18 to 21 years. Evidence is needed to inform the ongoing debate on this policy. Although proponents believe that a higher MLA will protect youths from the harms of cannabis use, critics argue that it will push them back to the illegal market. Objective: To investigate changes in youth cannabis use after an increase in MLA for cannabis in Quebec. Design, Setting, and Participants: This cross-sectional study with difference-in-differences analysis compared changes in cannabis use among youths aged 15 to 20 years in Quebec vs all other Canadian provinces before and after Quebec's increase in MLA. All estimates in descriptive and regression analyses were weighted. Nationally representative data from the National Cannabis Surveys 2018-2020 were used. Intervention: Increase in MLA for cannabis in Quebec implemented in January 2020. Main Outcomes and Measures: Past-3-month cannabis use. Results: The study sample included 1005 respondents (mean [SD] age, 17.5 [1.7] years; 50.2% [SD, 50.0%] male). After policy implementation, the increase in past-3-month cannabis use among youths aged 18 to 20 was 16.4 percentage points (95% CI, -27.3 to -5.5 percentage points; P = .01), or 51%, lower in Quebec than in other provinces. Meanwhile, no significant change in cannabis use among youths aged 15 to 17 years was found. The results were robust to several checks, including accounting for possible confounding effects of the COVID-19 pandemic on cannabis use. Conclusions and Relevance: In this study, an increase in the MLA from 18 to 21 years in Quebec was associated with a significantly lower increase in cannabis use among youths aged 18 to 20 years but no change in cannabis use among those aged 15 to 17 years. These findings can help to alleviate concerns that youths would switch to illegal markets in response to a higher MLA.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".