Does higher alcohol consumption affect attitudes towards alcohol control measures in Estonia?
Bibliographic record
Abstract
Objectives: To analyze whether higher alcohol consumption is associated with negative attitudes towards stricter alcohol control policy measures in Estonia. Study design: Cross-sectional analysis of nationally representative data from 2022 (n = 2059). Methods: Attitudes towards seven alcohol control measures and their association with high-risk alcohol consumption (>140 g absolute alcohol for men and >70 g for women per week) were analyzed using used descriptive statistics and binary logistic regression using nationally representative data on Estonian 15-74-year-old population. Results: In general, high-risk consumption associated with lower acceptance for alcohol control policies. Although men had higher prevalence of opposing alcohol control measure for every item considered, both men and women with high-risk alcohol consumption were significantly more likely to be against alcohol control measures in general even after accounting for the variation by demographic characteristics. Conclusions: As public opinion is detrimental to the successful implementation of alcohol policies, these findings emphasize the need to communicate alcohol-related harms to the public in order to increase awareness and support for alcohol control policies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".