Maternal and foetal outcomes among pregnant women hospitalised due to interpersonal violence: A population based study in Western Australia, 2002-2008
Bibliographic record
Abstract
BACKGROUND: Interpersonal violence is responsible for more ill-health and premature death in women under the age of 45 than other preventable health conditions, but findings concerning the effects of violence during pregnancy on both maternal and foetal health have been inconsistent. METHODS: A retrospective population-based cohort study was undertaken using linked data from the Hospital Morbidity Data Collection and the Western Australian Midwives' Notification System from 2002 to 2008. The aim was to determine the association between exposure to interpersonal violence during pregnancy and adverse maternal and foetal health outcomes at the population level. RESULTS: A total of 468 pregnant women were hospitalised for an incident of interpersonal violence during the study period, and 3,744 randomly selected pregnant women were included as the comparison group. The majority of violent events were perpetrated by the pregnant women's partner or spouse. Pregnant Indigenous women were over-represented accounting for 67% of all hospitalisations due to violence and their risk of experiencing adverse maternal outcomes was significantly increased compared to non-Indigenous women (adjusted odds ratio 1.53, 95% CI 1.21 to 1.95, p = 0.01). Pregnant women hospitalised for an incident of interpersonal violence sustained almost double the risk for adverse maternal complications than the non-exposed group (95% CI 1.34 to 2.18, p < 0.001). The overall risk for adverse foetal complications for pregnant women exposed to violence was increased two-fold (95% CI 1.50 to 2.76, p < 0.001). CONCLUSIONS: The risk of adverse health outcomes for both the mother and the baby increases if a pregnant woman is hospitalised for an incident of interpersonal violence during pregnancy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".