Short‐term clinical risk assessment and management: Comparing the Brockville Risk Checklist and Hamilton Anatomy of Risk Management
Bibliographic record
Abstract
The current article aims to examine the performance of two brief, dynamic risk measures - the Brockville Risk Checklist (BRC4) and one of two versions of the Hamilton Anatomy of Risk Management [HARM-FV and electronic HARM-FV (eHARM-FV)] - scored at regular clinical case conferences for forensic psychiatric patients in two different settings. The eHARM represents a first-in-class dynamic risk assessment tool using data analytics. Two studies are presented from two forensic psychiatric hospitals in Ontario, Canada. The first study compared the HARM-FV, scored by trained research staff, with the BRC4, scored concurrently by clinical teams, on 36 forensic inpatients. In the second study, trained research staff scored both the BRC4 and the eHARM-FV on 55 forensic inpatients. Both studies demonstrated that the BRC4 and both HARM-FV tools were moderately and positively correlated with each other, with higher agreement for similar domains and items. In both samples, the risk measures performed better at identifying individuals who engaged in repeated or more serious problematic behavior. The HARM-FV and eHARM-FV produced higher area under the curve values for subsequent behavior compared with the BRC4. All three tools were effective at detecting future aggression and adverse incidents. We did not directly compare the HARM-FV and eHARM-FV.
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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 | 0.017 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".