Comparing alcohol consumption in central and eastern Europe to other European countries
Bibliographic record
Abstract
AIMS: To give an overview of the volume of alcohol consumption, beverage preference, and patterns of drinking among adults (people 15 years and older) in central and eastern Europe (Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia) and to compare it to southern and western Europe, Russia and Ukraine. METHODS: Secondary data analysis. Consumption and preferred beverage type data for the year 2002 were taken from the WHO Global Status Report on Alcohol and the WHO Global Alcohol Database. RESULTS: Average consumption in central and eastern Europe is high with a relatively large proportion of unrecorded consumption ranging from one litre in Czech Republic and Estonia to 10.5 l in Ukraine. The proportion of heavy alcohol consumption (more than 40 g of pure alcohol per day) among men was the lowest in Bulgaria (25.8%) and the highest in Czech Republic (59.4%). Among women, the lowest proportion of heavy alcohol consumption was registered in Estonia (4.0%) and the highest in Hungary (16.0%). Patterns of drinking are detrimental with a high proportion of binge drinking, especially in the group of countries traditionally drinking vodka. In most countries, beer is now the most prevalent alcoholic beverage. CONCLUSIONS: Other studies suggest that the population drinking levels found in central and eastern Europe are linked with higher levels of detrimental health outcomes. Known effective and cost-effective programs to reduce levels of risky drinking should, therefore, be implemented, which may, in turn, lead to a reduction of alcohol-attributable burden of disease.
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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 | 0.003 | 0.003 |
| 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) | 0.001 | 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".