Rate and characteristics of men with an intellectual disability in pre‐trial detention
Bibliographic record
Abstract
BACKGROUND: Various lines of research point to the overrepresentation of individuals with intellectual disability (ID) in the criminal justice system. Studies have also shown that individuals with ID are vulnerable to mental health problems. To date there have been no Canadian studies of individuals with an ID in the criminal justice system. METHOD: The present study reports on the sociodemographic, mental health and criminological characteristics of 281 individuals with an ID from a Canadian study of men in a pre-trial holding centre. RESULTS: Almost 19% of the men had a probable ID, and nearly one-third (29.9%) were in the borderline IQ range. As was the case for their non-ID counterparts, the mean age of the probable ID group was in the early 30s, few were employed, and most had low incomes. Individuals in the probable and borderline ID groups had lower rates of high school completion than those in the average intellectual ability range. Over 60% of individuals with an ID had a substance use disorder, and 1 in 5 was intoxicated at the time of assessment. These rates were similar across groups, and to those found in the literature. A large majority of individuals with ID had a previous conviction, and most had previously committed a violent offence. CONCLUSIONS: Among other limitations, the sample may not have been entirely representative of the total population of men in the pre-trial detention centre, given the relatively high refusal rate (39.5%). Results are discussed in terms of orienting criminological and mental health services as a function of the level of intellectual and adaptive functioning of individuals with ID.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".