Maternal and fetal outcomes of intimate partner violence associated with pregnancy in the Latin American and Caribbean region
Bibliographic record
Abstract
BACKGROUND: Very high rates of intimate partner violence during pregnancy (IPV-P) are reported in Latin America and the Caribbean (LAC) but data on prevalence and obstetric-related outcomes are limited. OBJECTIVES: To conduct a literature review on risk factors, prevalence, and adverse obstetric-related outcomes of IPV-P in LAC. SEARCH STRATEGY: Systematic review of studies in MEDLINE (1946-2012) and LILACS (1982-2012), and hand searching of reference lists of included studies. Search terms were variations of partner abuse and pregnancy in LAC. SELECTION CRITERIA: Studies were excluded if they did not include IPV-P prevalence or if the perpetrator was not an intimate partner. DATA COLLECTION AND ANALYSIS: Study quality was assessed via US Preventive Services Task Force criteria. MAIN RESULTS: In the 31 studies included, prevalence rates ranged from 3% to 44%. IPV-P was significantly associated with unintended pregnancies and adverse maternal (depression, pregnancy-related symptom distress, inadequate prenatal care, vaginal bleeding, spontaneous abortion, gestational weight gain, high maternal cortisol, hypertension, pre-eclampsia, STIs) and infant (prematurity, low birth weight, neonatal complications, stillbirth) outcomes (grade II-2 and 3 evidence). CONCLUSIONS: IPV-P is highly prevalent in LAC, with poor obstetric-related outcomes. Clinicians must identify women experiencing IPV-P and institute appropriate interventions and referrals to avoid its deleterious consequences.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".