Version 3 of the Historical‐Clinical‐Risk Management‐20 (HCR‐20<sup>V3</sup>): Relevance to Violence Risk Assessment and Management in Forensic Conditional Release Contexts
Bibliographic record
Abstract
The conditional release of insanity acquittees requires decisions both about community risk level and the contextual factors that may mitigate or aggravate risk. This article discusses the potential role of the newly revised Historical-Clinical-Risk Management-20 (HCR-20, Version 3) within the conditional release context. A brief review of the structured professional judgment (SPJ) approach to violence risk assessment and management is provided. Version 2 of the HCR-20, which has been broadly adopted and evaluated, is briefly described. New features of Version 3 of the HCR-20 with particular relevance to conditional release decision-making are reviewed, including: item indicators; ratings of the relevance of risk factors to an individual's violence; risk formulation; scenario planning; and risk management planning. Version 3 of the HCR-20 includes a number of features that should assist evaluators and decision-makers to determine risk level, as well as to anticipate and specify community conditions and contexts that may mitigate or aggravate risk. Research on the HCR-20 Version 3 using approximately 800 participants across three settings (forensic psychiatric, civil psychiatric, correctional) and eight countries is reviewed.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".