The Police Role in Domestic Homicide Prevention: Lessons From a Domestic Violence Death Review Committee
Bibliographic record
Abstract
This study examined the role of police in domestic homicide cases reviewed by a multidisciplinary death review committee in Ontario, Canada. Examining the 219 domestic homicide case summaries, this study explored the difference between homicides with, and without, prior police contact. Results indicated that police contacted cases had 63% more risk factors present compared with cases without prior police contact, with 80% of police-involved cases having 10 or more risk factors. Police cases had unique risk factors present including a failure to comply with authority, access to victims after risk assessments, prior threats to kill victims (including with a weapon), history of domestic violence (DV), extreme minimization of DV, addiction concerns, and an escalation of violence. Cases involving child homicide have unique child-specific risk factors such as custody disputes, threats to children, and abuse during pregnancy. Overall, there was a lack of formal risk assessments conducted. Implications are discussed in terms of police intervention being a critical opportunity for risk assessment, safety planning, and risk management. Although there is no certainty in predicting that lives would have been saved, the level of risk presented calls for enhanced efforts at assessment and intervention for adult victims and their children.
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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 | 0.112 | 0.213 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".