Randomized and non-randomized evidence for the effect of compulsory community and involuntary out-patient treatment on health service use: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is limited randomized controlled trial (RCT) evidence for compulsory community treatment. Other study methods may clarify their effectiveness. We reviewed RCT and non-RCT evidence for the effect of compulsory community treatment on hospital admissions, bed-days, compliance and out-patient contacts. METHOD: A systematic review of RCTs, controlled before-and-after (CBA) studies, and interrupted time series (ITS) analyses. Meta-analysis of RCTs. RESULTS: Eight papers covering five studies (two RCTs and three CBAs) met inclusion criteria (total n=1108). There was no statistical difference in 12-month admission rates between subjects on involuntary out-patient treatment and controls. Survival analyses of time to admission were equivocal. All five studies reported decreases in the number of bed-days following involuntary out-patient treatment but this only reached statistical significance in one situation; patients receiving the intervention were less likely to have admissions of over 100 days. There was no difference in treatment adherence between the intervention and control groups in either RCT or two of the CBA studies. However, the third CBA study reported a statistically significant increase of nearly five visits in the mean number of overall contacts in the involuntary out-patient treatment group. CONCLUSIONS: The evidence for involuntary out-patient treatment in reducing either admissions or bed-days is very limited. It therefore cannot be seen as a less restrictive alternative to admission. Other effects are uncertain. Evaluation of a wide range of outcomes should be included if this type of legislation is introduced.
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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 | 0.035 | 0.095 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.039 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".