Prevalence of and Potential Influencing Factors for Alcohol Dependence in Europe
Bibliographic record
Abstract
Alcohol use disorders (AUDs), and alcohol dependence (AD) in particular, are prevalent and associated with a large burden of disability and mortality. The aim of this study was to estimate prevalence of AD in the European Union (EU), Iceland, Norway, and Switzerland for the year 2010, and to investigate potential influencing factors. The 1-year prevalence of AD in the EU was estimated at 3.4% among people 18-64 years of age in Europe (women 1.7%, men 5.2%), resulting in close to 11 million affected people. Taking into account all people of all ages, AD, abuse and harmful use resulted in an estimate of 23 million affected people. Prevalence of AD varied widely between European countries, and was significantly impacted by drinking cultures and social norms. Correlations with level of drinking and other drinking variables and with major known outcomes of heavy drinking, such as liver cirrhosis or injury, were moderate. These results suggest a need to rethink the definition of AUDs.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".