Comparing alcohol policy environments in high‐income jurisdictions with the International Alcohol Control Policy Index
Bibliographic record
Abstract
INTRODUCTION: Considerable evidence exists on the most effective policy to reduce alcohol harm; however, a tool and index to allow comparisons of policy status of the most effective policies between similar jurisdictions and change over time within a jurisdiction has not been widely used. The International Alcohol Control (IAC) Policy Index is designed to address this gap and monitor the alcohol policy environment with regard to four effective policy domains (tax/pricing, availability, marketing and drink driving). METHODS: This study compares IAC Policy Index scores across 11 high-income jurisdictions: Aotearoa (Māori language name for New Zealand); Australia; Finland; Norway; the Netherlands; (Republic of Ireland; Lithuania; Ontario; Alberta; Quebec; British Columbia). Collaborators in the 11 high-income jurisdictions populated the online Alcohol Policy Tool with available indicators. The team in Aotearoa New Zealand sought to validate information and worked with collaborators to clarify any uncertainties in the data. RESULTS: Lithuania, Norway, Finland and Ireland scored above average on the IAC Policy Index. The jurisdictions varied in terms of the strength of policy in different domains, with drink driving legislation showing the greatest consistency and marketing the strongest relationship between stringency of policy and impact on the ground. DISCUSSION AND CONCLUSIONS: Results in high-income jurisdictions suggested the IAC Policy Index provides a useful overview of core alcohol policy status, allows for comparisons between jurisdictions and has the potential to be useful in alcohol policy debate.
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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 | 0.000 | 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".