Alcohol Control Policy in Europe: Overview and Exemplary Countries
Bibliographic record
Abstract
Alcohol is a major risk factor for burden of disease. However, there are known effective and cost-effective alcohol control policies that could reduce this burden. Based on reviews, international documents, and contributions to this special issue of International Journal of Environmental Research and Public Health (IJERPH), this article gives an overview of the implementation of such policies in the World Health Organization (WHO) European Region, and of best practices. Overall, there is a great deal of variability in the policies implemented between countries, but two countries, the Russian Federation and Lithuania, have both recently implemented significant increases in alcohol taxation, imposed restrictions on alcohol availability, and imposed bans on the marketing and advertising of alcohol within short time spans. Both countries subsequently saw significant decreases in consumption and all-cause mortality. Adopting the alcohol control policies of these best-practice countries should be considered by other countries. Current challenges for all countries include cross-border shopping, the impact from recent internet-based marketing practices, and international treaties.
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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 | 0.001 | 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 | 0.001 |
| 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".