Compulsory Community and Involuntary Outpatient Treatment for People With Severe Mental Disorders
Bibliographic record
Abstract
There is controversy as to whether compulsory community treatment (CCT) for people with severe mental illness (SMI) reduces health service use or improves clinical outcome and social functioning. To examine the effectiveness of CCT for people with SMI. We searched the Cochrane Schizophrenia Group's Trials Register and Science Citation Index (2003, 2008, 2012, and 2013). We obtained all references of identified studies and contacted authors where necessary. All relevant randomized controlled clinical trials (RCTs) of CCT compared with standard care for people with SMI (mainly schizophrenia and schizophrenia-like disorders, bipolar disorder, or depression with psychotic features). Standard care could be voluntary treatment in the community or another preexisting form of compulsory community treatment such as supervised discharge. We found 3 trials with a total of 752 people. Two trials compared a form of CCT called 'Outpatient Commitment' (OPC) versus standard voluntary care, whereas the third compared Community Treatment Orders with intermittent supervised discharge. CCT was no more likely to result in better service use, social functioning, mental state, or quality of life compared with either standard voluntary or supervised care. However, people receiving CCT were less likely to be victims of crime than those on voluntary care. Further research is indicated into the effects of different types of CCT as these results are based on 3 relatively small trials.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".