Incremental prediction of intimate partner violence: An examination of three risk measures.
Bibliographic record
Abstract
Improvements in the risk prediction of domestic violence against intimate partners have the potential to inform policing practices in the prevention of further victimization. The present study examined the incremental predictive validity of 3 measures of risk for intimate partner violence (IPV)-Spousal Assault Risk Assessment (SARA), Ontario Domestic Assault Risk Assessment (ODARA), and the Family Violence Investigative Report (FVIR)-for IPV, general violence, and general recidivism outcomes. The sample featured 289 men and women who were reported to police for IPV and followed up approximately 3 years post release. Archival ratings of the 3 measures demonstrated that SARA scores showed incremental validity for IPV recidivism, ODARA scores incrementally predicted general violence, and both tools incrementally predicted general recidivism. The FVIR did not incrementally predict any outcomes. Fine grained analyses demonstrated that the Psychosocial Adjustment domain of the SARA contributed most uniquely to the prediction of IPV. Survival analysis supported the use of the SARA and ODARA in tandem for appraising risk for IPV or general criminal recidivism. Calibration analyses using logistic regression modeling also demonstrated 3-year recidivism estimates for SARA and ODARA scores. Implications for the use of multiple tools in clinical practice are discussed, particularly for combining the SARA and ODARA measures to augment IPV risk assessment and management. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".