Impact of Adverse Childhood Experiences in the Manifestation of Intimate Partner Violence Victimization and Perpetration among College Students: Gendered Perspective
Bibliographic record
Abstract
Purpose This study examined the effect of five types of adverse childhood experiences (ACEs) on Intimate Partner Violence (IPV) perpetration and IPV victimization for male and female students (N = 3,682).Method The data came from seven universities in the U.S. and Canada using multivariate logistic regression models.Results For female students, peer victimization (PV), childhood maltreatment (CM), exposure to domestic violence (EDV), drug use, and depression were significantly associated with higher odds of IPV perpetration. For male students, IPV perpetration was significantly associated with PV, CM, other physical and sexual violence (PSV), and alcohol use. IPV victimization was significantly associated with CM, EDV, PSV, drug use, and depression for female students. For male students, CM, PSV, alcohol use, and depression were associated with higher odds of IPV victimization.Discussion Because gender-specificity exists in the transmission process of ACEs and other risk factors for IPV, developing gender-specific approaches to IPV prevention and intervention on college campuses is necessary.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".