The benefits and harms of community treatment orders for people diagnosed with psychiatric illnesses: A rapid umbrella review of systematic reviews and meta-analyses
Bibliographic record
Abstract
AIMS: Community treatment orders have been introduced in many jurisdictions with increasing use over time. We conducted a rapid umbrella review to synthesise the quantitative and qualitative evidence from systematic reviews and/or meta-analyses of their potential harms and benefits. METHODS: A systematic search of Medline, PubMed, Embase and PsycINFO for relevant systematic reviews and/or meta-analyses. Where available, participants on community treatment orders were compared with controls receiving voluntary psychiatric treatment. This review is registered with PROSPERO (CRD42023398767) and the Open Science Framework (https://osf.io/zeq35). RESULTS: In all, 17 publications from 14 studies met the inclusion criteria. Quantitative synthesis of data from different systematic reviews was not possible. There were mixed findings on the effects of community treatment orders on health service use, and clinical, psychosocial or forensic outcomes. Whereas uncontrolled evidence suggested benefits, results were more equivocal from controlled studies and randomised controlled trials showed no effect. Any changes in health service use took several years to become apparent. There was evidence that better targeting of community treatment order use led to improved outcomes. Although there were other benefits, such as in mortality, findings were mostly rated as suggestive using predetermined and standardised criteria. Qualitative findings suggested that family members and clinicians were generally positive about the effect of community treatment orders but those subjected to them were more ambivalent. Any possible harms were under-researched, particularly in quantitative designs. CONCLUSIONS: The evidence for the benefits of community treatment orders remains inconclusive. At the very least, use should be better targeted to people most likely to benefit. More quantitative research on harms is indicated.
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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 | 0.096 | 0.226 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.022 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".