Risk of death after type of intimate partner violence (IPV) involvement
Bibliographic record
Abstract
Objective and ApproachMost studies have focused on women’s IPV victimization but perpetration and bi-directional violence remain understudied. Using linked criminal justice and population registers in Manitoba, Canada, we assessed the risk of death according to the role in IPV incidents (accused-only, victim-only, bidirectional, none). In this retrospective matched cohort study, we assembled a cohort of 212,068 adults who were followed from April 2004 to March 2023 to assess IPV incidents and subsequent death. Those involved in an IPV incident were 1:3 matched to persons with no history of IPV based on birth year, sex and marital status at the time of the incident. ResultsMen comprised 85% of accused-only, 21% of victim-only, and 50-51% in the other two groups. Overall, compared to those without IPV involvement, the adjusted Hazard Ratios for all-cause mortality were 1.38 [95% Confidence Intervals (CI): 1.28, 1.49] for accused-only, 1.39 (1.29, 1.50) for victim-only and 1.45 (1.19, 1.77) for bidirectional IPV. The associations were stronger among women, particularly that of bidirectional violence [1.24 (0.97, 1.60) among men and 1.92 (1.39, 2.66) among women]. Similar patterns were found for intentional mortality [0.96 (0.56, 1.64) among men and 2.43 (1.27, 4.65) among women]. ConclusionsThere are clear sex inequities in IPV involvement. Any type IPV involvement is associated with higher risk of death, particularly among women, who comprise most of the victims. ImplicationsUse of linked criminal justice data allows studying the consequences of IPV victimization, perpetration and bidirectional violence and monitor gender inequities over time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".