Does minimum pricing reduce alcohol consumption? The experience of a Canadian province
Bibliographic record
Abstract
AIMS: Minimum alcohol prices in British Columbia have been adjusted intermittently over the past 20 years. The present study estimates impacts of these adjustments on alcohol consumption. DESIGN: Time-series and longitudinal models of aggregate alcohol consumption with price and other economic data as independent variables. SETTING: British Columbia (BC), Canada. PARTICIPANTS: The population of British Columbia, Canada, aged 15 years and over. MEASUREMENTS: Data on alcohol prices and sales for different beverages were provided by the BC Liquor Distribution Branch for 1989-2010. Data on household income were sourced from Statistics Canada. FINDINGS: Longitudinal estimates suggest that a 10% increase in the minimum price of an alcoholic beverage reduced its consumption relative to other beverages by 16.1% (P < 0.001). Time-series estimates indicate that a 10% increase in minimum prices reduced consumption of spirits and liqueurs by 6.8% (P = 0.004), wine by 8.9% (P = 0.033), alcoholic sodas and ciders by 13.9% (P = 0.067), beer by 1.5% (P = 0.043) and all alcoholic drinks by 3.4% (P = 0.007). CONCLUSIONS: Increases in minimum prices of alcoholic beverages can substantially reduce alcohol consumption.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".