Women's experiences of domestic and family violence screening during pregnancy
Bibliographic record
Abstract
Background: Implementing domestic and family violence (DFV) screening, support, and prevention within maternity services is becoming common practice but women's experiences of screening are not routinely evaluated. Aim: To determine pregnant women's views and experiences of DFV screening by midwives. Methods: Pregnant women (n = 210) attending an antenatal service were surveyed about their experiences of screening and asked to complete three new measures: Beliefs about DFV Screening; Non-disclosure of DFV; and Midwifery Support. Results: Most women (92.3% n = 194) reported being asked about DFV during pregnancy by a midwife. Twelve (5.8%) respondents had/were experiencing DFV but not all disclosed during screening. A quarter (24.1% n = 49) had experienced abuse during childhood. The scales were reliable and factor analysis established validity. Women reported positive Beliefs (Mean 35.38, SD 3.63 range 19–40) and views about Midwifery Support (Mean 24.88, SD 3.08 range 18–30). There was less agreement about why some women do not disclose DFV (Mean 21.97, SD 4.27, range 8–30). Lower scores on the Comfort factor of the Beliefs Scale occurred if a woman was abused as a child (t [199] = −2.283, p = .23), or experiencing violence now (t [199] = −2.283, p = .016). Written comments (n = 75) revealed support for screening, but routine enquiry needed to be explained, occur in the context of a trusting relationship and be confidential. Conclusions: Women value screening, even if DFV is not disclosed. Exploring women's experiences of DFV screening is central to ensuring quality care and responding sensitively if DFV is disclosed.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".