The Impact of Prenatal and Postpartum Partner Violence on Maternal Mental Health: Results from the Community Child Health Network Multisite Study
Bibliographic record
Abstract
Background: Intimate partner violence (IPV) negatively impacts maternal and infant health, yet few studies assess violence at multiple time points during the childbearing year. Methods: Using data on 2018 women from the multisite Community Child Health Network (CCHN), this study assesses the relationship between past-year IPV (reported at 1 and 12 months postpartum) and maternal depression and perceived stress measured 1 year postpartum. Past-year IPV was measured using a modified version of the HITS (Hurts, Insults, Threatens, and Screams) assessment; depression was assessed using the Edinburgh Postnatal Depression Scale; perceived stress was assessed by the Perceived Stress Scale. Multivariable logistic regression models estimated risk for depression and estimated stress scores among women reporting exposure to IPV at one or both time points compared to those unexposed to IPV, adjusting for maternal age and household income. Results: At 1 month postpartum, 36% of participants reported past-year IPV. At 12 months postpartum, 48% of participants reported IPV at either or both interviews. Compared to women reporting no IPV at either time point, violence reported at both time points was associated with symptoms of postpartum depression (considered a score of ≥13) (odds ratio [OR] = 2.06, confidence intervals [CI] = 1.21–3.53) and increased levels of perceived stress (β = 1.64, CI = 0.86–2.41) at 12 months postpartum after adjusting for baseline depression and perceived stress, respectively. Conclusions: These findings expand on previous research by showing that IPV, particularly when recurrent, is associated with increased risk of depression and perceived stress 1 year postpartum. Routine IPV screening paired with linkage to support services throughout prenatal and postpartum care is one strategy to address this important problem.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".