The Associations Among Male - Perpetrated Partner Violence, Wives’ Psychological Distress and Children’s Behavior Problems: A Structural Equation Modeling Analysis
Bibliographic record
Abstract
The purpose of this investigation was to test a multivariate family model in order to gain insight into the consequences of male-perpetrated marital violence, specifically the effects of this violence on the wife’s mental health and their children’s behavior problems. Data from 260 male veteranfemale partner dyads who had one or more children were drawn from the National Vietnam Veterans Readjustment Study and analyzed using structural equation modeling techniques. The five latent variables in the structural model were the husband’s report of family functioning, the wife’s report of family functioning, husband-to-wife marital violence, wife’s psychological distress, and child behavior problems. In the structural model of best fit, male-perpetrated marital violence was associated with the wife’s level of psychological distress. However, most of this association flowed indirectly through the intermediary variable of wife’s assessment of family functioning. Additionally, the wife’s psychological distress was the sole path that linked marital violence to the child’s behavior. A series of followup analyses revealed that these findings were invariant across child behavior problem type (i.e., internalizing vs. externalizing problems) and child gender. These findings suggest that interparental violence does impact children, but that it does so through its effect on the psychological state of the mother. Accordingly, these findings reinforce the importance of programs that provide services to women and their children who are living in violent households.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".