Das Ontario Domestic Assault Risk Assessment (ODARA) – Validität und autorisierte deutsche Übersetzung eines Screening-Instruments für Risikobeurteilungen bei Intimpartnergewalt
Bibliographic record
Abstract
On average one third of all women are victimised by intimate partner violence at least once in their life. Many women are affected repeatedly. To efficiently reduce the risk of repeated violence a reliable and valid risk assessment is needed. To date several risk assessment tools have been published in the field of intimate partner violence. There is a strong demand for such tools to be applicable by a wider range of practitioners (e.g., police officers, hospital and victim services' staff) to reliably assess cases of intimate partner violence and recommend further interventions. By developing the Ontario Domestic Assault Risk Assessment (ODARA) it was the authors' aim to provide a brief risk assessment tool which can be scored on the basis of only few and easily collectable information and which efficiently discriminates between low-risk and high-risk offenders of intimate partner violence. Previous replication studies found moderate to good and on average higher power of discrimination of the ODARA compared to other risk assessment tools in the field. However, for the German speaking countries robust findings are still lacking. In the present publication a scientific and authorised translation of the ODARA is provided.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".