INTIMATE PARTNER VIOLENCE AND NEW-ONSET DEPRESSION: A LONGITUDINAL STUDY OF WOMEN'S CHILDHOOD AND ADULT HISTORIES OF ABUSE
Bibliographic record
Abstract
BACKGROUND: Studies indicate that women victims of intimate partner violence are at increased risk for poor mental health. This research disentangled the effect of partner violence on new-onset depression and psychosis spectrum symptoms from effects of child maltreatment and other confounding factors, including substance abuse and antisocial personality. METHODS: Participants were 1,052 mothers involved in the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally representative cohort of families followed prospectively. To test the directionality of associations between partner violence and depression, only women without a history of depression at the beginning of the study were considered (n = 978). Partner violence and mental health were assessed during face-to-face interviews with women across three time points. RESULTS: Four of 10 women reported being the victim of violence from their partner in a 10-year period. They represent 33% of our cohort and they account for 51% of new-onset depression. These women had a twofold increase in their risk of suffering from new-onset depression once the effect of childhood maltreatment, socioeconomic deprivation, antisocial personality, and young motherhood were controlled. Women who were abused both in childhood and adulthood were four to seven times more likely to suffer from depression than never-abused women. We observed similar associations with psychosis spectrum symptoms. CONCLUSIONS: Women victims of partner violence account for more than their share of depression. Findings strengthen existing evidence that partner violence independently contributes to women's poor mental health. Psychological difficulties among a considerable number of women could be reduced by stopping partner 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".