The Alcohol Environment Protocol: A new tool for alcohol policy
Bibliographic record
Abstract
INTRODUCTION AND AIM: To report data on the implementation of alcohol policies regarding availability and marketing, and drink driving, along with ratings of enforcement from two small high-income to three high-middle income countries, and one low-middle income country. METHOD: This study uses the Alcohol Environment Protocol, an International Alcohol Control study research tool, which documents the alcohol policy environment by standardised collection of data from administrative sources, observational studies and interviews with key informants to allow for cross-country comparison and change over time. RESULTS: All countries showed adoption to varying extents of key effective policy approaches outlined in the World Health Organization Global Strategy to Reduce the Harmful Use of Alcohol (2010). High-income countries were more likely to allocate resources to enforcement. However, where enforcement and implementation were high, policy on availability was fairly liberal. Key Informants judged alcohol to be very available in both high- and middle-income countries, reflecting liberal policy in the former and less implementation and enforcement and informal (unlicensed) sale of alcohol in the latter. Marketing was largely unrestricted in all countries and while drink-driving legislation was in place, it was less well enforced in middle-income countries. CONCLUSION: In countries with fewer resources, alcohol policies are less effective because of lack of implementation and enforcement and, in the case of marketing, lack of regulation. This has implications for the increase in consumption taking place as a result of the expanding distribution and marketing of commercial alcohol and consequent increases in alcohol-related harm.
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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 | 0.129 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.108 | 0.039 |
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".