Alcohol’s harm to others in Switzerland in the year 2011/2012
Bibliographic record
Abstract
Background and Aims: Alcohol consumption not only causes harm to the drinker, but also affects other people around the drinker. The prevalence of being affected by others’ drinking was estimated in Switzerland for the year 2011/2012. Methods: Data were collected in the context of the Addiction Monitoring in Switzerland (AMIS). Two thousand four hundred and seventy four subjects participated in computer assisted telephone interviews in a representative survey of the Swiss adult (15+ years) population. Results: In the past 12 months, 52.2% of the Swiss population was affected by others’ drinking in some way. Young adults were affected more often than older persons (p < 0.001) and men were more often affected than women (OR = 0.84; p < 0.05). Compared to abstainers, low risk drinkers (OR = 1.46; p < 0.05), risky single occasion drinkers (RSOD) (OR = 1.95; p < 0.001), and heavy drinkers (OR = 1.88; p < 0.01) were more often affected. The dominant type of harm was harm in public space (like harassment or being afraid because of others’ drinking) which was reported by 45.7% of the sample. Conclusion: More than half of the Swiss population was affected by others’ drinking in at least one way. Alcohol use is associated with psychological and physiological burden for persons other than the drinkers themselves.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".