Intellectual and developmental disabilities in Ontario's criminal justice and forensic mental health systems: Using data to tell the story
Bibliographic record
Abstract
BACKGROUND: International studies show that adults with intellectual and developmental disabilities (IDD) are disproportionately represented in the criminal justice and forensic mental health systems; however, it is difficult to capture their involvement across systems in any one jurisdiction. AIMS: The current study aimed to estimate the prevalence of IDD across different parts of the criminal justice and forensic mental health systems in Ontario and to describe the demographic and clinical profiles of these individuals relative to their counterparts without IDD. METHODS: This project utilised administrative data to identify and describe the demographic and clinical characteristics of adults with IDD and criminal justice or forensic involvement across four sectors: federal correctional facilities, provincial correctional facilities, forensic inpatient mental health care and community mental health programmes. Questions were driven by and results were contextualised by a project advisory group and people with lived experience from the different sectors studied, resulting in a series of recommendations. RESULTS: Adults with IDD were over-represented in each of the four settings, ranging from 2.1% in federal corrections to 16.7% in forensic inpatient care. Between 20% (forensic inpatient) and 38.4% (provincial corrections) were under the age of 25 and between 34.5% (forensic inpatient) and 41.8% (provincial corrections) resided in the lowest income neighbourhoods. Medical complexity and rates of co-occurring mental health conditions were higher for people with IDD than those without IDD in federal and provincial corrections. CONCLUSIONS: Establishing a population-based understanding of people with IDD within these sectors is an essential first step towards understanding and addressing service and care needs. Building on the perspectives of people who work in and use these systems, this paper concludes with intervention recommendations before, during and after justice involvement.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".