Exploring Enhancements to Intimate Partner Violence Risk Assessment: Leveraging Insights from Decision Tree Analyses
Bibliographic record
Abstract
Intimate partner violence (IPV) represents the largest proportion of violence in most women's lives, and it affects millions of individuals and families across Canada and around the world.Research indicates that current IPV risk assessments do not meaningfully outperform general-violence risk assessments.The purpose of the current study was to improve IPV risk assessments, specifically, to better understand whether IPV specialists and generalists are different and, additionally, to determine the extent to which IPV-salient risk factors should be considered, or differently weighted, in predicting recidivism.The sample (N = 911) included men who were referred to outpatient treatment or recruited from an employment agency between 1976 and 1994.Using recidivism data obtained in 1999 for a subset of 343 cases, more than 700 simple and composite potential predictor variables were examined.Approximately 53% of men were identified as IPV specialists, and 31% recidivated.Using classification and regression tree (CART) analyses, 22 variables were identified as differentiating between specialists and generalists, and 13 variables were found to differentiate between recidivists and non-recidivists in the training samples, respectively.A random forest procedure was used to reduce the pool of variables, resulting in trees with overall classification accuracy of 72.1% in the specialization model and 62.0% in the recidivism model.The models generalized with some success to the testing samples.Finally, an assessment was made of the weighting of IPV-salient to generalviolence risk factors.When considered separately, the IPV-salient and general-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 | 0.023 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| 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".