The Average Predictive Validity of Intimate Partner Violence Risk Assessment Instruments
Bibliographic record
Abstract
The field of intimate partner violence (IPV) risk assessment (predicting recidivism, lethality) is fast growing, and the majority of research examining the predictive validity of IPV risk assessment instruments has been conducted in the past decade. This study examines the average predictive validity weighted by sample size of five stand alone IPV risk assessment instruments that have been validated in multiple research studies using the Receiver Operating Characteristic Area Under the Curve (AUC). The Ontario Domestic Assault Risk Assessment (ODARA) has the highest average weighted AUC (=.666, k=5) followed, in order of most to least predictive, by the Spousal Assault Risk Assessment (SARA; AUC=.628, k=6), the Danger Assessment (DA; AUC=.618, k=4), the Domestic Violence Screening Inventory (DVSI; AUC=.582, k=3), and the Kingston Screening Instrument for Domestic Violence (K-SID; AUC=.537, k=2). The effect size for the average AUCs for IPV risk assessment instruments is small, with the exception of a medium effect size for the ODARA. Of the 20 measures of predictive validity included in this analysis, the risk assessment was administered correctly in nine (45%). IPV risk assessment is relatively new, and the use of proxy instruments and utilization of risk assessment instruments in settings for which they were not created is widespread. While waiting for a more rigorous body of research, factors in addition to predictive validity must be taken into consideration (e.g., setting, outcome, skills of the assessor, access to information) when choosing which risk assessment instrument is appropriate for use in a particular practice setting.
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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 | 0.067 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".