How Attitudes toward Alcohol Policies Differ across European Countries: Evidence from the Standardized European Alcohol Survey (SEAS)
Bibliographic record
Abstract
Alcohol policy endorsements have changed over time, probably interacting with the implementation and effectiveness of alcohol policy measures. The Standardized European Alcohol Survey (SEAS) evaluated public opinion toward alcohol policies in 20 European locations (19 countries and one subnational region) in 2015 and 2016 (n = 32,641; 18–64 years). On the basis of the SEAS report, we investigated regional differences and individual characteristics related to categories of alcohol policy endorsement. Latent class analysis was used to replicate cluster structure from the SEAS report and to examine individual probabilities of endorsement. Hierarchical quasi-binomial regression models were run to analyze the relative importance of variables of interest (supranational region, gender, age, educational achievement, and drinking status) on class endorsement probability, with random intercepts for each location. The highest support for alcohol control policies was recorded in Northern countries, which was in contrast to the Eastern countries, where the lowest support for control policies was found. Across all locations, positive attitudes toward control policies were associated with the female gender, older age, and abstaining from alcohol. Our findings underline the need to communicate alcohol-related harm and the implications of alcohol control policies to the public in order to increase awareness and support for such policies in the long run.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".