Liberal alcohol legislation: does it amplify the effects among Swiss men of person‐related risk factors on heavy alcohol use?
Bibliographic record
Abstract
AIM: To estimate the statistical interactions between alcohol policy strength and the person-related risk factors of sensation-seeking, antisocial personality disorder and attention-deficit/hyperactivity disorder related to heavy alcohol use. DESIGN: Cross-sectional survey. SETTING: Young Swiss men living within 21 jurisdictions across Switzerland. PARTICIPANTS: A total of 5701 Swiss men (mean age 20 years) participating in the Cohort Study on Substance Use Risk Factors (C-SURF). MEASUREMENTS: Outcome measures were alcohol use disorder (AUD) as defined in the DSM-5 and risky single-occasion drinking (RSOD). Independent variables were sensation-seeking, antisocial personality disorder (ASPD), attention-deficit/hyperactivity disorder (ADHD) and an index of alcohol policy strength. FINDINGS: Alcohol policy strength was protective against RSOD [odds ratio (OR) = 0.91 (0.84-0.99)], while sensation-seeking and ASPD were risk factors for both RSOD [OR = 1.90 (1.77-2.04); OR = 1.69 (1.44-1.97)] and AUD [OR = 1.58 (1.47-1.71); OR = 2.69 (2.30-3.14)] and ADHD was a risk factor for AUD [OR = 1.08 (1.06-1.10)]. Significant interactions between alcohol policy strength and sensation-seeking were identified for RSOD [OR = 1.06 (1.01-1.12)] and AUD [OR = 1.06 (1.01-1.12)], as well as between alcohol policy strength and ASPD for both RSOD [OR = 1.17 (1.03-1.31)] and AUD [OR = 1.15 (1.02-1.29)]. These interactions indicated that the protective effects of alcohol policy strength on RSOD and AUD were lost in men with high levels of sensation-seeking or an ASPD. No interactions were detected between alcohol policy strength and ADHD. CONCLUSION: Stronger alcohol legislation protects against heavy alcohol use in young Swiss men, but this protective effect is lost in individuals with high levels of sensation-seeking or having an antisocial personality disorder.
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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) | 0.000 | 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".