Multirater Reliability of the Historical, Clinical, and Risk Management-20
Bibliographic record
Abstract
The assessment and management of risk for future violence is a core requirement of mental health professionals in many settings. Despite an increasing need for violence risk assessments across diverse contexts, little is known regarding the ecological validity of many widely used risk assessment schemes or the level of reliability with which actual practicing clinicians score these instruments. The current study investigated the interrater reliability of the Historical, Clinical, and Risk Management-20 (HCR-20), a widely used structured professional tool to assess violence risk, among 21 practicing clinicians in a forensic psychiatric program in Ontario, Canada. Results suggest that clinicians with varying professional training backgrounds and experience were able to rate the HCR-20 with good to excellent levels of reliability across three patients who varied in risk level. Consistent with studies investigating rater reliability for research purposes, we found that the risk management scale of the HCR-20 was the most challenging for clinicians to rate reliably. Importantly, results from generalizability theory analyses revealed that less than 3% of the variance in HCR-20 total scores and summary risk ratings is attributable to rater effects, whereas the majority of variance is attributable to differences among patients.
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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 | 0.045 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".