The clinical profiles of forensic inpatients with intellectual disabilities in a specialized unit
Bibliographic record
Abstract
Purpose Individuals with intellectual disability (ID), mental health needs and criminal justice system involvement are likely to be admitted to forensic units; however, not all of those individuals are served in that system. It is, therefore, important to understand the profile of those admitted to non‐forensic specialized units for individuals with ID and mental health issues. This paper aims to address this issue. Design/methodology/approach Demographic, clinical and criminal profiles of individuals discharged over nine years from a specialized dual diagnosis program were reviewed to delineate clinical subgroups. Findings A total of 20 out of 84 total discharges were identified as having past or current criminal justice system involvement. The most common offence was assault and 60 per cent of these individuals had admissions longer than one year. Subgroups by psychiatric diagnosis differed in their age, legal status, offence history, and length of hospital stay, as well as in therapeutic interventions and discharge process. Research limitations/implications The results suggest that inpatients with ID and criminal justice system involvement present with unique treatment, support and risk management needs based on psychiatric diagnosis. The number of individuals in clinical subgroups was low, thus further research is needed to determine if the observed patterns hold true in bigger samples. Originality/value The study delineates the complexity and heterogeneity of treatment and supports needs of individuals with intellectual disabilities and offending behaviour.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".