A Multilevel Analysis of Regional and Gender Differences in the Drinking Behavior of 23 Countries
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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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".