Alcohol‐related negative consequences among drinkers around the world
Bibliographic record
Abstract
AIMS: This paper examines (i) gender and country differences in negative consequences related to drinking; (ii) relative rates of different consequences; and (iii) country-level predictors of consequences. DESIGN SETTING AND PARTICIPANTS: Multi-level analyses used survey data from the Gender, Alcohol, and Culture: An International Study (GENACIS) collaboration. MEASUREMENTS: Measures included 17 negative consequences grouped into (i) high endorsement acute, (ii) personal and (iii) social. Country-level measures included average frequency and quantity of drinking, percentage who were current drinkers, gross domestic product (GDP) and Human Development Index (HDI). FINDINGS: Overall, the three groupings of consequences were reported by 44%, 12% and 7% of men and by 31%, 6% and 3% of women, respectively. More men than women endorsed all consequences, but gender differences were greatest for consequences associated with chronic drinking and social consequences related to male roles. The highest prevalence of consequences was in Uganda and lowest in Uruguay. Personal and social consequences were more likely in countries with higher usual quantity, fewer current drinkers and lower scores on GDP and HDI. However, significant interactions with individual-level quantity indicated a stronger relationship between consequences and usual quantity among drinkers in countries with lower quantity, more current drinkers and higher scores on GDP and HDI. CONCLUSIONS: Both gender and country need to be taken into consideration when assessing adverse drinking consequences. Individual measures of alcohol consumption and country-level variables are associated with experiencing such consequences. Additionally, country-level variables affect the strength of the relationship between usual quantity consumed by individuals and adverse consequences.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".