Change in the Relationship Between Drinking Alcohol and Risk of Violence Among Adolescents and Young Adults: A Nationally Representative Longitudinal Study
Bibliographic record
Abstract
AIMS: To quantify the relationship between alcohol and violence with increasing age. METHODS: Data were from The National Longitudinal Study of Adolescent to Adult Health (ADD Health) of 20,386 people representative of the US population. Mean age at the first wave of interviews was 16.2 years, with subsequent interviews mean of 1, 6.3 and 12.9 years later. We used random-effects models and predictive marginal effects of the association between varying quantities of alcohol consumption and violence while controlling for possible confounders. RESULTS: Violence was reported by 19.1% of participants at wave I but just 2.1% at wave IV. The random-effects model showed that consuming 1-4 drinks on each occasion was associated with a modest increase in risk of violence in both males (odds ratio (OR) 1.36, 95% CI 1.13-1.63) and females (OR 1.33, 95% CI 1.03-1.72). For consumption of five or more drinks on each occasion, the risk remained similar for females (OR 1.40 (0.99-1.97)) but increased considerably for males (OR 2.41 (1.96-2.95)). Predictive marginal effects models confirmed that violence rates decreased with age. CONCLUSIONS: Alcohol is most strongly linked to violence among adolescents, so programmes for primary prevention of alcohol-related violence are best targeted towards this age group, particularly males who engage in heavy episodic drinking.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".