An assessment of federal alcohol policies in Canada and priority recommendations: Results from the 3rd Canadian Alcohol Policy Evaluation Project
Bibliographic record
Abstract
OBJECTIVE: To systematically assess the Canadian federal government's current alcohol policies in relation to public health best practices. METHODS: The 2022 Canadian Alcohol Policy Evaluation (CAPE) Project assessed federal alcohol policies across 10 domains. Policy domains were weighted according to evidence for their relative impact, including effectiveness and scope. A detailed scoring rubric of best practices was developed and externally reviewed by international experts. Policy data were collected between June and December 2022, using official legislation, government websites, and data sources identified from previous iterations of CAPE as sources. Contacts within relevant government departments provided any additional data sources, reviewed the accuracy and completeness of the data, and provided amendments as needed. Data were scored independently by members of the research team. Final policy scores were tabulated and presented as a weighted overall average score and as unweighted domain-specific scores. RESULTS: Compared to public health best practices, the federal government of Canada scored 37% overall. The three most impactful domains-(1) pricing and taxation, (2) marketing and advertising controls, and (3) impaired driving countermeasures-received some of the lowest scores (39%, 10%, and 40%, respectively). Domain-specific scores varied considerably from 0% for minimum legal age policies to 100% for controls on physical availability of alcohol. CONCLUSION: Many evidence-informed alcohol policies have not been adopted, or been adopted only partially, by the Canadian federal government. Urgent adoption of the recommended policies is needed to prevent and reduce the enormous health, social, and economic costs of alcohol use in Canada.
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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 | 0.089 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".