The Role of Sex and Age on Pre-drinking: An Exploratory International Comparison of 27 Countries
Bibliographic record
Abstract
AIMS: This exploratory study aims to model the impact of sex and age on the percentage of pre-drinking in 27 countries, presenting a single model of pre-drinking behaviour for all countries and then comparing the role of sex and age on pre-drinking behaviour between countries. METHODS: Using data from the Global Drug Survey, the percentages of pre-drinkers were estimated for 27 countries from 64,485 respondents. Bivariate and multivariate multilevel models were used to investigate and compare the percentage of pre-drinking by sex (male and female) and age (16-35 years) between countries. RESULTS: The estimated percentage of pre-drinkers per country ranged from 17.8% (Greece) to 85.6% (Ireland). The influence of sex and age on pre-drinking showed large variation between the 27 countries. With the exception of Canada and Denmark, higher percentages of males engaged in pre-drinking compared to females, at all ages. While we noted a decline in pre-drinking probability among respondents in all countries after 21 years of age, after the age of 30 this probability remained constant in some countries, or even increased in Brazil, Canada, England, Ireland, New Zealand and the United States. CONCLUSIONS: Pre-drinking is a worldwide phenomenon, but varies substantially by sex and age between countries. These variations suggest that policy-makers would benefit from increased understanding of the particularities of pre-drinking in their own country to efficiently target harmful pre-drinking behaviours.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".