Healthcare Use for Violent Injury After Intimate Partner Violence Identified Through the Justice System: A Data Linkage Study
Bibliographic record
Abstract
IntroductionRoutine health care information systems only capture a portion of violence against women because some victimized women may not seek health care and some events may not require medical attention. Population-based estimates of the risk of violent injury (VI) among women with a history of intimate partner victimization (IPV) are lacking. Objectives and ApproachTo determine the risk of violent injury following IPV among women living in Manitoba, Canada, 2004-2016. Linked administrative justice, healthcare, and social databases were used. Exposure began after a woman was first involved with the Manitoba Justice system as a victim of IPV, assessed through provincial prosecution and disposition records. IPV victims (n= 20,469) were matched to three non-victims (n= 61,407) on age, relationship status and place of residence at the date of the IPV incident. The main outcomes were first health care use for violent injury and violent death. Outcomes were assessed through emergency department, hospital and vital statistics records. Conditional Cox Regression was used to obtain Hazard Ratios with 95% confidence intervals (CI). ResultsThe crude risk of VI was 8.5 per 1000 women among non-victims and 55.8 among victims of IPV. Compared to non-victims, IPV victims were 3.8 [95% confidence interval (CI): 3.4, 4.3] times more likely to suffer IIIO and 4.5 [95% CI: 2.3, 9.0] times to have a violent death, after adjustment. Victims had approximately half the risk of VI if the accused is on probation. Conclusion / ImplicationsJustice System-identified victims of IPV are at higher risk of assault and violent death than women not exposed to IPV. Justice involvement represents an opportunity for prevention of violent injury and homicide among IPV victims.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".