Geographical variation in the prevalence of heavy drinking in young Swiss men
Bibliographic record
Abstract
BACKGROUND: Not much is known about how much geographical units matter for heavy alcohol consumption and how much of the geographical variations are explained by characteristics such as institutional alcohol policies and regional economic conditions. The study aim was to address these gaps considering three types of heavy alcohol consumption. METHODS: Analyses were based on data collected on 5879 men (age: 20.0 years, standard deviation: 1.2) years participating in the Cohort Study on Substance Use Risk Factors in Switzerland. Generalized linear mixed models were used to assess overall prevalence, geographical variations in prevalence across geographical units (institutional units, economic micro regions, linguistic regions, urban/rural status), and explanatory variables in three different types of heavy alcohol consumption (heavy weekend drinking, heavy workweek drinking, heavy volume drinking). RESULTS: The overall prevalence for heavy weekend drinking was 46.8%, 10.8% for heavy volume drinking and 3.6% for heavy workweek drinking. The extent and locations of geographical variation in prevalence rates were contingent upon the type of alcohol consumption. Institutional alcohol policies explained substantial geographical variations in heavy weekend drinking, but not in heavy workweek or heavy volume drinking. Regional economic conditions were not related to alcohol consumption. CONCLUSIONS: Different types of heavy alcohol consumption are determined by different geographical units. Alcohol policies protectively impact the major drinking style of heavy weekend drinking, but not other low prevalence forms of heavy drinking. Research and public health efforts must take into account these differences between types of alcohol consumption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".