The Factor Structure of Intimate Partner Violence Risk
Bibliographic record
Abstract
To improve our understanding of the latent constructs of intimate partner violence (IPV) risk, we explored the underlying factor structure of combined items from three IPV risk assessment tools and examined whether the factors predict recidivism outcomes. Data were examined for 251 adult men who were charged with violence against their past or current female intimate partners and whose files were referred for a comprehensive threat assessment from 2010 to 2016 in Canada. Results suggested six underlying risk factors, two of which significantly predicted IPV, any violent, and any recidivism outcomes in a 4-year average follow-up with 227 men. However, only one factor ( Antisocial Patterns and Psychosocial Adjustment ) independently predicted IPV and any violent recidivism over time above and beyond other factors. Our findings indicate room to further improve current IPV risk assessment measures and support the call for informative causal theories of IPV recidivism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".