The increasing influence of risk assessment on forensic patient review board decisions.
Bibliographic record
Abstract
Previous studies of decisions about forensic patients' placement in secure hospitals indicate some changes over time in the use of empirically supported risk factors. Our aim was to investigate whether, in more recent cases, risk assessment instruments were cited by a forensic patient review board or by the clinicians who made recommendations to the board and whether there was evidence of an association between risk assessment results and either dispositions or recommendations. Among review board hearings held in 2009-2012 pertaining to 63 different maximum security patients found not criminally responsible on account of mental disorder in Ontario, Canada, dispositions were most strongly associated with psychiatrists' testimony, consistent with previous studies. However, dispositions were associated with the scores on the Violence Risk Appraisal Guide (VRAG), such that transferred patients had a lower risk of violent recidivism than detained patients. An association between clinical opinions and risk assessment results was also evident and significantly larger than in previous research. There was no evidence that risk assessment was cited selectively in higher risk cases or when scores were concordant with the review board decision. This research may provide a baseline for studies of the effect of 2014 legislation introducing a high-risk designation for forensic patients in Canada. We recommend further efforts to measure the effect of nonpharmacological treatment participation and in-hospital security decisions on forensic decision-making. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.086 | 0.563 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".