Gender specific trends in alcohol use: cross-cultural comparisons from 1998 to 2006 in 24 countries and regions
Bibliographic record
Abstract
Objective: To examine trends in the prevalence of monthly alcohol \nuse and lifetime drunkenness among 15 year olds in 20 European countries, the Russian Federation, Israel, the United States of America, and Canada. \nMethods: Alcohol use prevalence and drunkenness were assessed \nin the Health Behavior in School-aged Children Survey \nconducted in each country in 1998, 2002, and 2006. Trends were \ndetermined using the Cochran-Mantel-Haenszel test for trends. \nResults: Average monthly alcohol use across all countries declined \nfrom 45.3 % to 43.6 % and drunkenness declined from \n37.2 % to 34.8. There was substantial variability across countries, \nwith decreases in some countries and increases or no \nchange in use or drunkenness in others. The overall decline \nwas greater among boys, from 41.2 % to 36.7 % than among \ngirls, 33.3 % to 31.9 %. In most of the countries where drinking \nor drunkenness increased, it was due mainly to increases \namong girls. \nConclusions: Trends in alcohol use and drunkenness varied by \ncountry. Drinking and drunkenness remained higher among \nboys than girls, but the gap between boys and girls declined and \ngirls appear to be catching up with boys in some countries.
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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 | 0.001 |
| 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) | 0.001 | 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".