Support for alcohol policies among drinkers in Mongolia, New Zealand, Peru, South Africa, St Kitts and Nevis, Thailand and Vietnam: Data from the International Alcohol Control Study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: A 2010 World Health Assembly resolution called on member states to intensify efforts to address alcohol-related harm. Progress has been slow. This study aims to determine the magnitude of public support for 12 alcohol policies and whether it differs by country, demographic factors and drinking risk (volume consumed). DESIGN AND METHODS: Data are drawn from seven countries participating in the International Alcohol Control Study which used country-specific sampling methods designed to obtain random, representative samples. The weighted total sample comprised 11 494 drinkers aged 16-65 years. RESULTS: Drinking risk was substantial (24% 'increased' risk and 16% 'high' risk) and was particularly high in South Africa. Support varied by alcohol policy, ranging from 12% to 96%, but was above 50% for 79% of the possible country/policy combinations. Across countries, policy support was generally higher for policies addressing drink driving and increasing the alcohol purchase age. There was less support for policies increasing the price of alcohol, especially when funds were not earmarked. Policy support differed by country, and was generally higher in the five middle-income countries than in New Zealand. It also differed by age, gender, education, quantity/frequency of drinking, risk category and country income level. DISCUSSION AND CONCLUSIONS: We found a trend in policy support, generally being highest in the low-middle-income countries, followed by high-middle-income countries and then high-income countries. Support from drinkers for a range of alcohol policies is extensive across all countries and could be used as a catalyst for further policy action.
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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 | 0.000 | 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 | 0.000 |
| 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".