Confidence and accuracy in assessments of short-term risks presented by forensic psychiatric patients
Bibliographic record
Abstract
Forensic mental health professionals are asked to estimate with appropriate confidence the likelihood of adverse outcomes. But what is an ‘appropriate’ level of confidence? We examined this question in the context of short-term assessments of risk for violence, suicide, self-harm, and unauthorized leave. Using the Short-Term Assessment of Risk and Treatability (START), treatment team members (n = 23) completed 331 assessments of 137 forensic psychiatric patients appearing before the British Columbia Review Board over a six-month period. Assessors additionally indicated confidence in the accuracy of their risk assessments. Clinical–legal outcome data were collected prospectively for one year using a modified version of the Overt Aggression Scale (OAS). Overall, assessors were highly confident in the accuracy of their assessments; however, analyses revealed few differences in accuracy as a function of confidence. When significant differences were observed, higher confidence was associated with lower predictive accuracy. Findings suggest that assessors may benefit from feedback regarding predictive validity of past assessments and speak to the importance of comprehensive and ongoing training in risk assessment.
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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 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".