Alcohol May Not Cause Partner Violence But It Seems to Make It Worse: A Cross National Comparison of the Relationship Between Alcohol and Severity of Partner Violence
Bibliographic record
Abstract
This study assesses whether severity of physical partner aggression is associated with alcohol consumption at the time of the incident, and whether the relationship between drinking and aggression severity is the same for men and women and across different countries. National or large regional general population surveys were conducted in 13 countries as part of the GENACIS collaboration. Respondents described the most physically aggressive act done to them by a partner in the past 2 years, rated the severity of aggression on a scale of 1 to 10, and reported whether either partner had been drinking when the incident occurred. Severity ratings were significantly higher for incidents in which one or both partners had been drinking compared to incidents in which neither partner had been drinking. The relationship did not differ significantly for men and women or by country. We conclude that alcohol consumption may serve to potentiate violence when it occurs, and this pattern holds across a diverse set of cultures. Further research is needed that focuses explicitly on the nature of alcohol's contribution to intimate partner aggression. Prevention needs to address the possibility of enhanced dangers of intimate partner violence when the partners have been drinking and eliminate any systemic factors that permit alcohol to be used as an excuse. Clinical services for perpetrators and victims of partner violence need to address the role of drinking practices, including the dynamics and process of aggressive incidents that occur when one or both partners have been 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".