A Multilevel Analysis of Regional and Gender Differences in the Drinking Behavior of 23 Countries
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Bibliographic record
Abstract
Introduction: Drinking behavior differs not only among countries, but also among regions within a country. However, the extent of such variation and the interplay between gender and regional differences in drinking have not been explored and are addressed in this study. Methods: Data stem from 105,061 individuals from 23 countries of the GENACIS data set. The outcomes were heavy drinking (10/20 g or more of pure ethanol per day for women/men), and risky single occasion drinking (RSOD) (5+ drinks per occasion) at least monthly. Analyses used binary logistic mixed models. Variance at specific levels was measured by the intra-class correlation coefficient (ICC). Gender differences in outcomes were measured using gender ratios. Results: Country-level ICC was 0.13 (95% CI: 0.09–0.18) for heavy drinking and 0.16 (95% CI: 0.10–0.26) for RSOD. Within-country regional-level ICC for heavy drinking and RSOD was 0.02 (95% CI: 0.009–0.05; 0.01–0.04, respectively), implying that 2% of variation in heavy drinking and RSOD was explained by regional variation. Variance in drinking indicators was larger for women compared to men across countries. Gender ratios were higher in low- and middle-income countries. Conclusions: Regional variations in risky drinking were more often present in low- to middle-income countries as well as in a few higher-income countries, and could be due to cultural and demographic differences. Variations in gender differences were larger on the country level than on the regional level, with lower-income countries showing larger differences. These results can help to better identify specific high-risk groups for prevention strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it