Young Swiss men’s risky single-occasion drinking: Identifying those who do not respond to stricter alcohol policy environments
Bibliographic record
Abstract
BACKGROUND: Previous research has demonstrated a preventive effect of the alcohol policy environment on alcohol consumption. However, little is known about the heterogeneity of this effect. Our aim was to examine the extent of heterogeneity in the relationship between the strictness of alcohol policy environments and heavy drinking and to identify potential moderators of the relationship. METHODS: Cross-sectional data from 5986 young Swiss men participating in the cohort study on substance use risk factors (C-SURF) were analysed. The primary outcome was self-reported risky single-occasion drinking in the past 12 months (RSOD, defined as 6 standard drinks or more on a single occasion at least monthly). A previously-used index of alcohol policy environment strictness across Swiss cantons was analysed in conjunction with 21 potential moderator variables. Random forest machine learning captured high-dimensional interaction effects, while individual conditional expectations captured the heterogeneity induced by the interaction effects and identified moderators. RESULTS: Predicted subject-specific absolute risk reductions in RSOD risk ranged from 16.8% to - 4.2%, indicating considerable heterogeneity. Sensation seeking and antisocial personality disorder (ASPD) were major moderators that reduced the preventive relationship between stricter alcohol policy environments and RSOD risk. They also were associated with the paradoxical observation that some individuals displayed increased RSOD risk in stricter alcohol policy environments. CONCLUSION: Whereas stricter alcohol policy environments were associated with reduced average RSOD risk, additionally addressing the risk conveyed by sensation seeking and ASPD would deliver an interlocking prevention mix against young Swiss men's RSOD.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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 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".