A systematic review and meta-analysis of the effect of community treatment orders on aggression or criminal behaviour in people with a mental illness
Bibliographic record
Abstract
AIMS: There has been concern about violent acts and other criminal behaviour by people with a possible history of mental health problems. We therefore assessed the effects of community treatment orders (CTOs) on self-, third-party-, and agency-reported criminal behaviour when compared to voluntary treatment. METHODS: A systematic search of PubMed/Medline, Embase, PsycINFO and criminal justice bibliographic databases for observational or randomised controlled trials (RCTs) comparing CTO cases with controls receiving voluntary psychiatric treatment. Relevant outcomes were reports of violence and aggression or contacts with the criminal justice system such as arrests and court appearances. RESULTS: Thirteen papers from 11 studies met inclusion criteria. Nine papers came from the United States and four from Australia. Two papers were of RCTs. Results for all outcomes were non-significant, the effect size declining as study design improved from non-randomised data on self-reported criminal behaviour, through third party criminal justice records and finally to RCTs. Similarly, there was no significant finding in the subgroup analysis of serious criminal behaviour. CONCLUSIONS: On the limited available evidence, CTOs may not address aggression or criminal behaviour in people with mental illness. This is possibly because the risk of violence is increased by comorbid or nonclinical variables, which are beyond the scope of CTOs. These include substance use, a history of victimisation or maltreatment, and the wider environment. The management of risk should therefore focus on the whole person and their community through social and public health interventions, not solely legislative control.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.026 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".