Criminal justice intimate partner victimization before, during and after pregnancy among birthing parents screened postnatally by public health nurses
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is a global problem, disproportionately affecting women. Pregnancy offers a unique opportunity to identify IPV. METHODS: This population-based data linkage study includes residents of Manitoba, Canada who gave birth from 2006-2020 and were eligible for the Families First Screening (FFS) (n = 194,280). The FFS is a universal screening tool completed typically within one week postpartum by public-health nurses, which includes questions on current history of relationship violence and distress, including pregnancy. We assessed the associations of these responses with criminal justice identified IPV (JIIPV) two years before through two years after pregnancy (2004-2022) using logistic and multinomial regressions models. RESULTS: The prevalence of JIIPV at any time around pregnancy among birthing parents without distress/violence during pregnancy was 36.6 per 1000 pregnancies. Compared to them, those who reported both violence and relationship distress had a prevalence of 474.5/1000 [Odds Ratio (OR): 23.8; 95 %CI: 21.4, 26.4, and adjusted OR (AOR): 5.4; 95 %CI: 4.8, 6.1]. Those who did not respond to distress/violence questions and those who did not participate in the FFS had also higher prevalence of JIIPV. Observed patterns persisted before, during and after pregnancy. CONCLUSION: Screening by public health nurses identifies families seriously affected by JIIPV around 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".