Forensic Inpatients with Low IQ and Psychiatric Comorbidities: Specificity and Heterogeneity of Psychiatric and Social Profiles
Bibliographic record
Abstract
While the prevalence of mental disorders in people with intellectual disabilities (ID) is well documented, there is less specific literature in the forensic domain. This study sought to clarify the psychiatric and criminological characteristics among Belgian French-speaker forensic inpatients with low IQ and mental health illnesses. To this end, we compared a low IQ group with mental health illnesses ( n = 69), low IQ group ( n = 56), and control group ( n = 165). Compared with controls, proportionally more inpatients low IQ with Mental Health Illnesses presented a psychiatric illness, particularly a mood disorder, and proportionally fewer presented a cluster C personality disorder. The findings highlight the specificity and heterogeneity of the psychiatric profile of this subgroup of patients. He also demonstrated that forensic patients with ID are not a homogeneous group. This emphasizes the importance of considering in the management of forensic ID patients the specific needs with regard to their psychopathological profile.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".