Changes in population‐level alcohol sales after non‐medical cannabis legalisation in Canada
Bibliographic record
Abstract
INTRODUCTION: There is considerable interest in whether individuals substitute cannabis for alcohol and in legalisation's potential to reduce or increase alcohol-attributable harms. This study aimed to determine whether non-medical cannabis legalisation in Canada was associated with initial changes in population-level alcohol consumption. METHODS: This observational population-based study described changes in alcohol sales in Canada between 2004 and 2022. We calculated annual changes in the per capita volume of pure ethanol sold in Canada. We used an interrupted time series approach to examine immediate and gradual changes in per capita price-adjusted alcohol retailer sales value (CAD$) and beer producer sales volume (litres of product) after legalisation. RESULTS: During 2004-2022, Canadians aged 15+ spent on average CAD $751 per year on alcoholic beverages containing 8.18 L of ethanol. Annual ethanol sales volumes decreased by 0.06 (95% confidence interval [CI] -0.08 to -0.04; p = 0.001) litres per capita annually for beer but increased by 0.05 (95% CI 0.04 to 0.07; p = 0.001) litres per capita annually for other beverages, leaving no significant trend for ethanol sales overall. Following non-medical legalisation in October 2018, there were no immediate (-0.1%, 95% CI -1.3 to 1.1; p = 0.82) or gradual changes (-0.1% monthly, 95% CI -0.3 to 0.0; p = 0.12) in alcohol retailer sales. DISCUSSION AND CONCLUSION: Canada's non-medical cannabis legalisation was not associated with significant changes in population-level alcohol sales. These findings do not support the idea that cannabis legalisation may result in declining alcohol use and harms through the substitution of cannabis for alcohol.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".