Evaluación de riesgo de asalto doméstico de ontario (ODARA) y el proyecto de defensa comunitaria (CAP) en la predicción y prevención de riesgo de violencia de pareja íntima: una revisión rápida
Bibliographic record
Abstract
Intimate partner violence is a problem that affects the development of what is considered the core of the formation of the individual, that is, the family. That is why this rapid review focuses on seeking empirical studies that test the validity and reliability of an intimate violence risk prediction tool such as the Ontario Domestic Assault Risk Assessment (ODARA), and empirical evidence of effectiveness of a program that provides victims of intimate violence with tools to prevent this type of violence, such as the Community Support Program (CAP). Of the 221 documents found, 14 were included in this review. In the studies, it was established that the ODARA is an actuarial tool that has empirical evidence in countries such as Canada, Switzerland, Australia, the United States, the Netherlands, and the United Kingdom, ranging from AUC = 0.643 to AUC = 0.744. The CAP has statistically significant results in the acquisition of resources by victims of intimate violence in areas such as satisfaction with social support, effectiveness in obtaining resources, access to community resources and quality of life. Both the ODARA and the CAP have empirical evidence that allows them to be established as adequate for the prediction and prevention of recidivism of intimate partner 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".