The clinical profile and service needs of psychiatric inpatients with intellectual disabilities and forensic involvement
Bibliographic record
Abstract
There is increasing recognition around the world that individuals with intellectual disabilities (ID) and mental health issues with forensic involvement are a particularly complex patient group whose needs are not well met. However, few studies have examined how these individuals may differ from other service users within a psychiatric hospital setting. Inpatients with ID and forensic involvement were compared to non-forensic inpatients with ID and to forensic inpatients without ID in terms of psychiatric diagnoses and clinical issues. Inpatients with ID and forensic involvement were younger, more often male, had greater lengths of stay, were more likely to have a personality disorder diagnosis and less likely to have a mood disorder diagnosis than their counterparts with ID. They were also similar to their forensic counterparts without ID with regards to demographics, but were less likely to have a substance abuse or psychotic disorder diagnosis. Furthermore, patients with ID and forensic involvement exhibit more severe symptoms, have fewer resources, and a higher recommended level of care than other forensic patients. Patients with ID and forensic involvement present with unique demographic and clinical profiles. The characteristics that set these individuals apart from other services users should be taken into account in order to better meet the needs of this complex group.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".