Use of the ODARA by police officers for intimate partner violence: Implications for practice in the field
Bibliographic record
Abstract
Despite research demonstrating the validity of the Ontario Domestic Assault Risk Assessment (ODARA) for appraising risk of subsequent intimate partner violence, gaps remain with regard to its actual use by police officers in the field. The primary goals of the current study were to assess the rate at which the ODARA was used by police officers for intimate partner violence (IPV) in the Canadian context and to identify factors associated with its use. The current study used 142 randomly selected police files meeting criteria for IPV from three police agencies in an Atlantic Canadian province, following province-wide training on domestic violence and the ODARA. The ODARA was used by police in 60.3% of cases, though more commonly when physical Violence was present at index (70%). Significant ODARA use variation was noted across the three police gencies. ODARAs were more likely administered when the suspect was using drugs/alcohol (76.4%), the incident was between parties in a current intimate relationship (67.0%), when physical violence occurred in the index event (70.6%), and when a weapon was used (84.2%). Decisions to arrest and recommend charges to the prosecutor were predicted by higher ODARA total scores, above and beyond the influence of the police organization, suspect/victim characteristics, and incident context variables. Results are discussed in the context of police discretion/decision-making and the need for stronger implementation and policy use guidelines for risk appraisal by police officers, which includes a better understanding of IPV and the ODARA.
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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 | 0.024 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".