Effectiveness of Community Treatment Orders: The International Evidence
Bibliographic record
Abstract
OBJECTIVE: Community treatment orders (CTOs) exist in more than 75 jurisdictions worldwide. This review outlines findings from the international literature on CTO effectiveness. METHOD: The article draws on 2 comprehensive systematic reviews of the literature published before 2013, then uses the same search terms to identify studies published between 2013 and 2015. The focus is on what the literature as a whole tells us about CTO effectiveness, with particular emphasis on the strength and weaknesses of different methodologies. RESULTS: The results from more than 50 nonrandomized studies show mixed results. Some show benefits from CTOs while others show none on the most frequently reported outcomes of readmission, time in hospital, and community service use. Results from the 3 existing randomized controlled trials (RCTs) show no effect of CTOs on a wider range of outcome measures except that patients on CTOs are less likely than controls to be a victim of crime. Patients on CTOs are, however, likely to have their liberty restricted for significantly longer periods of time. Meta-analyses pooling patient data from RCTs and high quality nonrandomized studies also find no evidence of patient benefit, and systematic reviews come to the same conclusion. CONCLUSION: There is no evidence of patient benefit from current CTO outcome studies. This casts doubt over the usefulness and ethics of CTOs. To remove uncertainty, future research must be designed as RCTs.
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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 | 0.023 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".