Screening and Interventions for Intimate Partner Violence in Pregnancy: The Perspective of Survivors
Bibliographic record
Abstract
OBJECTIVES: Intimate partner violence (IPV) poses serious risks to women's health, especially during pregnancy. Despite pregnancy being a key opportunity for IPV screening, various barriers often hinder health care provider efforts to screen for IPV. This study seeks to understand from IPV survivors how health care providers can better screen for and address IPV during pregnancy. METHODS: Women who had experienced IPV during pregnancy were recruited via Facebook ads. A website provided study details and a consent form. After consenting, participants completed a survey covering 4 main themes: personal demographics, IPV screening in pregnancy, barriers to disclosure, and interventions offered. Written comments were invited in addition to survey completion. RESULTS: This pilot study involved 23 participants who experienced IPV during pregnancy. Overall, 17 reported childhood abuse and 19 had experienced abuse outside of pregnancy. Although all participants supported IPV screening by health care providers during prenatal care, only 8 were screened. There was a preference for written tools over verbal assessments to enhance comfort and privacy. Barriers to disclosure included fear of partner retaliation, discomfort discussing IPV, concerns about confidentiality, and potential involvement of child protection services. Interventions offered were limited, with mental health support and social services identified as critical resources. CONCLUSIONS: Findings support the need for improved screening practices and comprehensive support systems to address the complex needs of pregnant individuals experiencing IPV, particularly given its prevalence and ties to childhood trauma. Enhancing standardized guidelines, health care provider training, and disclosure support are essential for promoting healthier outcomes for mothers and their children.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".