The impacts of selling alcohol in grocery stores in Ontario, Canada: a before after study
Bibliographic record
Abstract
OBJECTIVE: From 2015 to 2019, the Government of Ontario expanded privatized sales of alcohol, licensing 450 grocery stores to sell beer, cider, and wine. The impacts of a nearby grocery store gaining an alcohol license on adults' alcohol use in Ontario are examined, including whether impacts differed by gender. METHOD: = 30,486). Alcohol use outcomes included past-7-day number of standard drinks consumed, near-daily drinking (≥4 days/week), and heavy drinking (5+ drinks in men/4+ in women, at least once/month). Gender-specific difference-in-differences (DiD) analyses compared changes in alcohol use before and after intervention in intervention and control populations. RESULTS: Decreases in past-7-day drinks, near-daily drinking, and heavy drinking were observed after intervention in both intervention and control populations. At the 1,000 m level, adjusted DiD analyses showed past-7-day drinking in women (risk ratio = 1.21, 95% CI [0.88, 1.60]) and heavy drinking in men (odds ratio = 1.38, 95% CI [0.92, 2.08]) had effect sizes above 1, a relative increase over controls, although confidence intervals crossed 1. Findings did not indicate significant differences in alcohol use in intervention relative to controls for other alcohol use measures and at 1,500 m. CONCLUSIONS: Findings suggest no association between a partial alcohol deregulation initiative in Ontario and alcohol use from 2015 to 2019. It is important to monitor the impacts on alcohol use over time as further alcohol deregulation plans in Ontario and other jurisdictions are considered.
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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 | 0.000 |
| 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".