Clinical, Demographic, and Criminal Behavior Characteristics of Patients With Intellectual Disabilities in a Canadian Forensic Program
Bibliographic record
Abstract
Background: People with Neurodevelopmental Disorders (NDD) Intellectual Disability (ID), Autism-Spectrum Disorder (ASD) -- and forensic issues constitute a challenging clinical group that has been understudied in forensic settings. Methods: We assessed the characteristics of patients with NDD under the authority of the Ontario Review Board (ORB) in a large forensic program of a tertiary psychiatric hospital (excluding those with a cognitive disorder) and compared their characteristics with those of a non-NDD control group. Results: Among 510 adult ORB patients, 50 had a NDD diagnosis. NDD patients were: younger; with a lower level of education; less likely to have been married or employed, less likely to have a diagnosis of psychosis, less likely to be ‘not criminally responsible’; more likely to have committed a sexual offence, more likely to have a diagnosis of paraphilia, and more likely to be ‘unfit to stand trial’. They were also more likely to be treated in a secure unit, to have conflicts with co-patients, or to be involved in physical or verbal assault incidents. Conclusion: Our findings have major implications for clinicians, clinical leaders, and policymakers about the specific needs of patients with NDD presenting with forensic issues. In particular, their higher level of conflict suggests a need for higher levels of, or different, clinical support and risk management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".