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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".