The burden for clinical services of persons with an intellectual disability or mental disorder convicted of criminal offences: A birth cohort study of 14,605 persons followed to age 64
Bibliographic record
Abstract
BACKGROUND: Intellectual disability (ID), schizophrenia spectrum disorder (SSD), bipolar disorder (BD), substance use disorder (SUD), and other mental disorders (OMDs) are associated with increased risks of criminality relative to sex-matched individuals without these conditions (NOIDMD). To resource psychiatric, addiction, and social services so as to provide effective treatments, further information is needed about the size of sub-groups convicted of crimes, recidivism, timing of offending, antecedents, and correlates. Stigma of persons with mental disorders could potentially be dramatically reduced if violence was prevented. METHODS: A birth cohort of 14,605 persons was followed to age 64 using data from Swedish national health, criminal, and social registers. RESULTS: Percentages of group members convicted of violence differed significantly: males NOIDMD, 7.3%, ID 29.2%, SSD 38.6%, BD 30.7%; SUD 44.0%, and OMD 19.3%; females NOIDMD 0.8%, ID 7.7%, SSD 11.2%, BD 2.4%, SD 17.0%, and OMD 2.1%. Violent recidivism was high. Most violent offenders in the diagnostic groups were also convicted of non-violent crimes. Prior to first diagnosis, convictions (violent or non-violent) had been acquired by over 90% of the male offenders and two-thirds of the female offenders. Physical victimization, adult comorbid SUD, childhood conduct problems, and adolescent substance misuse were each associated with increased risks of offending. CONCLUSION: Sub-groups of cohort members with ID or mental disorders were convicted of violent and non-violent crimes to age 64 suggesting the need for treatment of primary disorders and for antisocial/aggressive behavior. Many patients engaging in violence could be identified at first contact with clinical services.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".