The Relationship Between the Big Five Personality Factors, Anger-hostility, and Alcohol and Violence in Men and Women: A Nationally Representative Cohort of 15,701 Young Adults
Bibliographic record
Abstract
Alcohol consumption is known to have a disinhibiting effect and is associated with a higher likelihood of aggressive behavior, especially among men. People with certain personality traits maybe more likely to behave aggressively when intoxicated, and there may also be variation by gender. We aimed to investigate whether the reason why men and women with certain personality traits are more likely to engage in violence may be because of their alcohol use.The Big Five personality traits and anger-hostility, alcohol consumption, and violence were measured by questionnaire in 15,701 nationally representative participants in the United States. We tested the extent to which alcohol mediates the relationship between personality factors and violence in men and women.We found that agreeableness was inversely associated with violence in both genders. Alcohol mediated approximately 11% of the effect in males, but there was no evidence of an effect in females. Anger-hostility was associated with violence in both sexes, but alcohol mediated the effect only in males. We also found that Extraversion was associated with violence and alcohol use in males and females. Alcohol accounted for 15% of the effect of extraversion on violence in males and 29% in females.The mechanism by which personality traits relate to violence may be different in men and women. Agreeableness and anger-hostility underpin the relationship between alcohol and violence in men, but not in women. Reducing alcohol consumption in men with disagreeable and angry/hostile traits would have a small but significant effect in reducing violence, whereas in women, reducing alcohol consumption among the extraverted, would have a greater effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".