Preventing mental health problems in mothers with a history of childhood abuse through reducing their risk of intimate partner violence: A causal mediation analysis in an Australian pregnancy cohort
Bibliographic record
Abstract
BACKGROUND: Women with a history of child maltreatment are more likely to experience intimate partner violence (IPV). Both are associated with poor mental health. Using causal mediation analysis, we aimed to estimate the potential reduction in mental health risk if mothers who experienced childhood abuse were not at increased risk of IPV. METHODS: Data was from a pregnancy cohort (n = 1507) followed up at 1, 4, 10 years postpartum. Child physical/sexual abuse were retrospectively reported, IPV was measured at each follow-up, and mental health symptoms (any depressive, anxiety or post-traumatic stress symptoms) at 10 years. We used a simulation-based g-computation to estimate change in mental health symptoms (outcome) if the prevalence of IPV in 1st and/or 4th year postpartum (mediator) for women with a history of childhood abuse (exposure) was reduced to the level of women not reporting abuse, with adjustment for baseline and intermediate confounders. FINDINGS: Childhood abuse led to increased IPV risk in 1st and/or 4th year postpartum (estimated risk ratio (RR):1.85; 95 %CI:1.59,2.12) and mental health problems at 10 years (RR:1.81; 95 %CI:1.49,2.13). For every 1000 mothers reporting childhood abuse, there were 140 extra cases (95 %CI:80,201) of mental health problems. Reducing their risk of IPV to that of women without an abuse history could prevent 31 (95 %CI:11,51) per 1000 of these cases (≈22 %). INTERPRETATION: Reducing IPV risk in early motherhood for mothers with a history of childhood abuse could substantially reduce mental health burden, with early childhood intervention likely to amplify benefits. Multilevel, systemic efforts are needed to interrupt cycles of family violence.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".