Violence Risk Assessment with the HCR-20<sup>V3</sup> in Legal Contexts: A Critical Reflection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The HCR-20V3 is a violence risk assessment tool that is widely used in forensic clinical practice for risk management planning. The predictive value of the tool, when used in court for legal decision-making, is not yet intensively been studied and questions about legal admissibility may arise. This article aims to provide legal and mental health practitioners with an overview of the strengths and weaknesses of the HCR-20V3 when applied in legal settings. The HCR-20V3 is described and discussed with respect to its psychometric properties for different groups and settings. Issues involving legal admissibility and potential biases when conducting violence risk assessments with the HCR-20V3 are outlined. To explore legal admissibility challenges with respect to the HCR-20V3, we searched case law databases since 2013 from Australia, Canada, Ireland, the Netherlands, New Zealand, the UK, and the USA. In total, we found 546 cases referring to the HCR-20/HCR-20V3. In these cases, the tool was rarely challenged (4.03%), and when challenged, it never resulted in a court decision that the risk assessment was inadmissible. Finally, we provide recommendations for legal practitioners for the cross-examination of risk assessments and recommendations for mental health professionals who conduct risk assessments and report to the court. We conclude with suggestions for future research with the HCR-20V3 to strengthen the evidence base for use of the instrument in legal contexts.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| 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 it