Social Inequalities and Gender Differences in the Experience of Alcohol-Related Problems
Bibliographic record
Abstract
AIMS: To examine the influence of country-level characteristics and individual socio-economic status (SES) on individual alcohol-related consequences. METHODS: Data from 42,655 men and women collected by cross-sectional surveys in 25 countries of the Gender, Alcohol and Culture: An International Study study were used. The individual SES was measured by the highest attained educational level. Alcohol-related consequences were defined as the self-report of at least one internal or one external consequence in the last year. The relationship between individuals' education and alcohol-related consequences was examined by meta-analysis. In a second step, the individual level data and country data were combined in multilevel models. As country-level indicators, we used the purchasing power parity of the gross national income (GNI), the Gini coefficient and the Gender Gap Index. RESULTS: Lower educated men and women were more likely to report consequences than higher educated men and women even after controlling for drinking patterns. For men, this relation was significant for both internal and external problems. For women, it was only significant for external problems. The GNI was significantly associated with reporting external consequences for men such that in lower income countries men were more likely to report social problems. CONCLUSION: The fact that problems accrue more quickly for lower educated persons even if they drink in the same manner can be linked to the social or environmental dimension surrounding problems. That is, those of fewer resources are less protected from the experience of a problem or the impact of a stressful life event.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".