An exploration of the determinants of risk formulation, care plan and disposition among older adults in the Ontario forensic psychiatry system: implication for practice
Bibliographic record
Abstract
BACKGROUND: Advances in medicine have led to an improvement in life expectancy, thus increasing the population of older individuals within the criminal justice system. This study investigates the determinants of risk formulation, care plan, and disposition among older adult forensic patients (OAFP) in Ontario, Canada. METHODS: This retrospective analysis utilized the Ontario Review Board database, focusing on 161 OAFP, aged 55 years and older. Hierarchical regression was used to analyze the relationship between changes in risk and six blocks of variables: sociodemographic characteristics (Block 1), circumstances during the index offense (Block 2), current clinical profile (Block 3), past psychiatric history and behavioral patterns (Block 4), criminal history and legal status (Block 5), and recent violent events (Block 6). RESULTS: The median age of patients was 61 years (IQR 58-67), with 83.4% being male. Schizophrenia was the most common diagnosis (68.3%), and 9.3% had neurocognitive disorders. The model with six blocks of factors explained 92% of the variability in risk change. Models 2 (blocks 1 and 2) and 4 (blocks 1-4) were statistically significant, explaining 34% (p = 0.010) and 22% (p = 0.018) of the variance in the change in risk of threat to public safety, respectively. OAFP with a significant risk to public safety were more likely to be inpatients and less likely intoxicated during their index offense. CONCLUSION: Resources, policies, and a supervised model of care to curtail behavioral risks are relevant to the care of OAFP. Innovative risk management models for OAFP are indicated.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".