Comparisons of Intergenerational Transmission of Violence Between Genders: A Multiple-Group Structural Equation Modeling
Bibliographic record
Abstract
This study aimed to test whether gender can impact the pathways of intergenerational transmission of violence (ITV) from childhood victimization by child abuse and neglect (CAN) to adulthood victimization and/or perpetration of intimate partner violence (IPV) and CAN using multiple-group structural equation modeling. A cross-sectional study using online self-administered questionnaires was conducted among parents of children aged <19 years in a suburb of Tokyo, Japan between January and February 2022. Childhood victimization by CAN, adulthood victimization, and/or perpetration of IPV and CAN were measured using the Family Poly-Victimization Screen (Japanese version). The Japanese version of the Kessler 6 and PCL5 were used to assess psychological distress. Data from 483 participants (231 males; 252 females) were used, indicating the moderating effects of gender on the ITV pathway from childhood to adulthood. The results for males indicated that childhood victimization of physical abuse was directly associated with adult IPV perpetration (β = .23). Although psychological distress did not mediate any ITV (indirect effects: p = .20–.60), it was directly associated with adulthood IPV victimization and CAN perpetration (β = .29, β = .16, and β = .21, respectively). In contrast, the results for females showed that childhood psychological and physical abuse were directly associated with adult CAN perpetration (β = .14 and β = .15, respectively). Psychological distress had a mediating role in the relationships between CAN childhood victimization, IPV adulthood victimization, and CAN perpetration (indirect effects: p = .03 and p = .02, respectively). This study highlights the importance of understanding the differences in the pathways of ITV between the two genders and the necessity to develop gender-informed interventions for abused children to prevent ITV in adulthood.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".