Risk Factors in Domestic Homicides: Identifying Common Clusters in the Canadian Context
Bibliographic record
Abstract
Little research has attempted to examine risk factor combinations when examining intimate partner violence. A variety of risk factors have been identified in domestic homicides, and it is recognized that risk of lethality may increase with the presence of more rather than less risk factors. This relationship is not necessarily linear, however. The objective of this study was to identify whether particular risk factor combinations are common in cases of domestic homicide. The study comprised 183 deaths that occurred between 2002 and 2012 and were reviewed by the Domestic Violence Death Review Committee, Office of the Chief Coroner of Ontario, Canada, with particular focus on the presence/absence of 40 empirically based risk factors. The analyses identified three distinct risk factor clusters that differed primarily by victim-perpetrator relationship and the likelihood of perpetrator suicide or attempts to commit suicide. Cases involving perpetrators currently in legal marriages or cohabitating with their victims were most common among the Non-Depressed/Non-Violent Cluster followed by the Depressed/Violent Cluster. In contrast, the majority of those in the Non-Depressed/Violent Cluster were estranged from their victims and the least likely to attempt/commit suicide. The study demonstrates that particular risk factor combinations are common in cases of domestic homicide. Future research should expand the number of risk factors examined, increase the sample size to further test cluster validity, and compare lethal and non-lethal intimate partner violence and homicide to allow for an examination of the clusters more unique to lethality. Prevention initiatives should emphasize the heterogeneity of domestic homicides and target specific interventions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".