The Canadian Alcohol Policy Evaluation project: Findings from a review of provincial and territorial alcohol policies
Bibliographic record
Abstract
INTRODUCTION: Effective alcohol control measures can prevent and reduce alcohol-related harms at the population level. This study aims to evaluate implementation of alcohol policies across 11 evidence-based domains in Canada's 13 jurisdictions. METHODS: The Canadian Alcohol Policy Evaluation project assessed all provinces and territories on 11 evidence-based domains weighted for scope and effectiveness. A scoring rubric was developed with policy and practice indicators and peer-reviewed by international experts. The 2017 data were collected from publicly-available regulatory documents, validated by government officials, and independently scored by team members. RESULTS: The average score for alcohol policy implementation across Canadian provinces and territories was 43.8%; Ontario had the highest (63.9%) and Northwest Territories the lowest (38.4%) jurisdictional scores. Only six of 11 policy domains had average scores above 50% with Monitoring and Reporting scoring the highest (62.8%) and Health and Safety Messaging the lowest (25.7%). A 2017 provincial/territorial current best practice score of 86.6% was calculated taking account of the highest scores for any individual policy indicators implemented in at least one jurisdiction across the country. DISCUSSION AND CONCLUSIONS: Most of the evidence-based alcohol policies assessed by the Canadian Alcohol Policy Evaluation project were not implemented across Canadian provinces and territories as of 2017, and many provinces showed declining scores since 2012. However, the majority of policies assessed have been implemented in at least one jurisdiction. Improved alcohol policies to reduce related harm are therefore achievable and could be implemented consistently across Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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".