Beyond Drinking: Differential Effects of Demographic and Socioeconomic Factors on Alcohol-Related Adverse Consequences across European 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/AIMS: Evidence underlines the importance of drinking patterns and individual characteristics in experiencing adverse alcohol-related consequences; however, little research has been conducted to explore who does and who does not experience consequences with similar drinking patterns. Using data from seven European countries, this study assesses the association between demographic and socioeconomic characteristics and six adverse consequences. METHODS: Conditional logistic regression models were estimated, cases (experiencing a consequence) being matched to controls (not experiencing the consequence) by drinking patterns. RESULTS: In general, protective effects with increasing age and being in a partnership were consistent. Gender effects were mixed, but mainly protective for women. Educational achievement and economic status showed consistent effects across countries, but different directions of effect across consequences. Consequences mostly associated with individual drinking pattern (injury, blackout, and loss of control over drinking) exhibited similar patterns of associations, but varying ones arose for consequences additionally influenced by societal reaction to drinking (guilt, role failure, and pressure to cut down drinking). CONCLUSION: Differences in strengths and directions of effects across consequences pointed to the possibility that the reporting of adverse consequences is not only influenced by alcohol consumption, but also by attributional processes related to demographic and socioeconomic statuses.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| 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