Income inequality and alcohol use: a multilevel analysis of drinking and drunkenness in adolescents in 34 countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Economic inequality has been hypothesized to be a health determinant, independent of poverty and household income. The goal of this study was to explore the contextual influences of income inequality on alcohol use and frequency of drunkenness in adolescents. METHODS: The Health Behaviour in School-aged Children study surveyed 162 305 adolescents (ages 11, 13 and 15 years) in 34 countries, providing self-report data on family affluence, alcohol consumption and episodes of drunkenness. Country-level data on income inequality and overall wealth were retrieved from the United Nations Development Program. RESULTS: Multilevel logistic regression revealed that 11- and 13-year-olds in countries of high income inequality consumed more alcohol than their counterparts in countries of low income inequality (after adjustment for sex, family affluence and country wealth). No such effect on alcohol consumption was found in 15-year-olds. Eleven-year-olds in countries of high income inequality reported more episodes of drunkenness than their counterparts in countries of low income inequality. No such effect of income inequality on drunkenness was found in 13- or 15-year-olds. CONCLUSIONS: Income inequality may have a contextual influence on the use of alcohol among younger adolescents. Findings suggest that economic policies that affect the distribution of wealth within societies may indirectly influence the use of alcohol during early and mid-adolescence.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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