Video Consultations and Safety App Targeting Pregnant Women Exposed to Intimate Partner Violence in Denmark and Spain: Nested Cohort Intervention Study (STOP Study)
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) during pregnancy is a public health issue with wide-ranging consequences for both the mother and fetus, and interventions are needed. Therefore, the Stop Intimate Partner Violence in Pregnancy (STOP) cohort was established with the overall aim to identify pregnant women exposed to IPV through digital screening and offer women screening positive for IPV a digital supportive intervention. OBJECTIVE: The aim of this study was to (1) introduce the design and profile of the STOP cohort study, (2) assess the feasibility of implementing digital IPV screening among pregnant women, and (3) assess the feasibility of implementing a digital supportive intervention targeting pregnant women exposed to IPV. METHODS: Pregnant women attending antenatal care in the Region of Southern Denmark and in Andalucía, Spain were offered digital screening for IPV using validated scales (Abuse Assessment Screen and Women Abuse Screening Tool). Women who screened positive were eligible to receive a digital supportive intervention. The intervention consisted of 3-6 video consultations with an IPV counselor and a safety planning app. In Denmark, IPV counselors were antenatal care midwives trained by a psychologist specialized in IPV, whereas in Spain, the counselor was a psychologist. RESULTS: Data collection started in February 2021 and was completed in October 2022. Across Denmark and Spain, a total of 19,442 pregnant women were invited for IPV screening and 16,068 women (82.65%) completed the screening. More women in Spain screened positive for exposure to IPV (350/2055, 17.03%) than in Denmark (1195/14,013, 8.53%). Among the women who screened positive, only 31.39% (485/1545) were eligible to receive the intervention with only 104 (21.4%) of these women ultimately receiving it. CONCLUSIONS: Digital screening for IPV among pregnant women is feasible in an antenatal care context in Denmark and Spain; however, a digital supportive intervention during pregnancy appears to have limited feasibility as only a minor subgroup of women who screened positive for eligibility received the intervention. More research is needed on how to best support pregnant women exposed to IPV if universal IPV screening is to be implemented in antenatal care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".