Have declines in the prevalence of young adult drinking in English-speaking high-income countries followed declines in youth drinking? A systematic review
Bibliographic record
Abstract
Background: Alcohol use in early adulthood is a significant public health concern. The prevalence of adolescent alcohol consumption has been declining in high-income English-speaking countries since the early 2000s. This review aims to examine whether this trend continues in young adulthood. Methods: We systematically searched Medline, PsycInfo and CINAHL and the grey literature. Eligible records reported the prevalence of alcohol consumption amongst 18-25-year-olds over a minimum three-year time frame in the United States (US), Canada, the United Kingdom, the Republic of Ireland, Australia and New Zealand. Results were described using narrative synthesis. Quality assessment was undertaken using the Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies. Results and conclusion: Thirty-two records from 22 different surveys were included. The prevalence of consumption amongst young adults fell in Australia, Ireland, and the United Kingdom and was stable in New Zealand and Canada. In the US, there was evidence of a decline in the prevalence of drinking among under-21s, but results for adults over the minimum purchase age were mixed. The prevalence of alcohol consumption in young adults appears to be broadly declining. This could lead to reduced rates of alcohol-related harms in the future. Further high-quality multinational surveys may help to confirm this trend.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".