Assessment and management of risk for intimate partner violence by police officers using the Spousal Assault Risk Assessment Guide.
Bibliographic record
Abstract
Intimate partner violence (IPV) is a crime that is present in all countries, seriously impacts victims, and demands a great deal of time and resources from the criminal justice system. The current study examined the use of the Spousal Assault Risk Assessment Guide, 2nd ed. (SARA; Kropp, Hart, Webster, & Eaves, 1995), a structured professional judgment risk assessment and management tool for IPV, by police officers in Sweden over a follow-up of 18 months. SARA risk assessments had significant predictive validity with respect to risk management recommendations made by police, as well as with recidivism as indexed by subsequent contacts with police. Risk management mediated the association between risk assessment and recidivism: High levels of intervention were associated with decreased recidivism in high risk cases, but with increased recidivism in low risk cases. The findings support the potential utility of police-based risk assessment and management of IPV, and in particular the belief that appropriately structured risk assessment and management decisions can prevent violence.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".