Gender Differences in the Relationship Between Alcohol and Violent Injury: An Analysis of Cross-National Emergency Department Data
Bibliographic record
Abstract
OBJECTIVE: The objectives of the present study were twofold: (1) to determine whether gender differences exist in the roles of drinking in the event (i.e., self-reported drinking before the injury and estimated blood alcohol concentration [BAC] captured after injury) and drinking pattern (i.e., heavy episodic drinking) in explaining violent versus nonviolent injuries and (2) to assess whether these gender differences vary by country. METHOD: Emergency department data were analyzed from 30 hospitals in 15 countries, as part of the Emergency Room Collaborative Alcohol Analysis Project and the World Health Organization Collaborative Study of Alcohol and Injuries. Interaction effects between gender and alcohol were tested in the prediction of violent versus nonviolent injury for each country. RESULTS: The bivariate analyses revealed significantly larger effects of drinking-in-the-event variables for men than for women in three countries (i.e., 6 hours before the injury in Argentina and having a positive BAC in Belarus and Spain). In the multivariate analyses, restricted to countries with sufficient sample sizes (i.e., Mexico, South Africa, and the United States), no significant gender differences were found between the drinking-in-the-event variables and violent injury. In the bivariate and multivariate analyses, a significant interaction effect between gender and heavy episodic drinking was found in the United States, indicating that heavy episodic drinking predicted violent injury for women but not for men. CONCLUSIONS: Although the results are preliminary, treatment and prevention programs may need to target both genders equally or perhaps even focus more on heavydrinking women, particularly in the United States.
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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 | 0.008 |
| 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 | 0.001 |
| 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".