Drinking patterns vary by gender, age and country‐level income: Cross‐country analysis of the International Alcohol Control Study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Gender and age patterns of drinking are important in guiding country responses to harmful use of alcohol. This study undertook cross-country analysis of drinking across gender, age groups in some high-and middle-income countries. DESIGN AND METHODS: Surveys of drinkers were conducted in Australia, England, Scotland, New Zealand, St Kitts and Nevis (high-income), Thailand, South Africa, Mongolia and Vietnam (middle-income) as part of the International Alcohol Control Study. Drinking pattern measures were high-frequency, heavier-typical quantity and higher-risk drinking. Differences in the drinking patterns across age and gender groups were calculated. Logistic regression models were applied including a measure of country-level income. RESULTS: Percentages of high-frequency, heavier-typical quantity and higher-risk drinking were greater among men than in women in all countries. Older age was associated with drinking more frequently but smaller typical quantities especially in high-income countries. Middle-income countries overall showed less frequent but heavier typical quantities; however, the lower frequencies meant the percentages of higher risk drinkers were lower overall compared with high-income countries (with the exception of South Africa). DISCUSSION AND CONCLUSIONS: High-frequency drinking was greater in high-income countries, particularly in older age groups. Middle-income countries overall showed less frequent drinking but heavier typical quantities. As alcohol use becomes more normalised as a result of the expansion of commercial alcohol it is likely frequency of drinking will increase with a likelihood of greater numbers drinking at higher risk levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".