Community mental health team management in severe mental illness: A systematic review
Bibliographic record
Abstract
BACKGROUND: Community mental health teams are now generally recommended for the management of severe mental illness but a comparative evaluation of their effectiveness is lacking. AIMS: To assess the benefits of community mental health team management in severe mental illness. METHOD: A systematic review was conducted of community mental health team management compared with other standard approaches. RESULTS: Community mental health team management is associated with fewer deaths by suicide and in suspicious circumstances (odds ratio=0.32, 95% Cl 0.09-1.12), less dissatisfaction with care (odds ratio=0.34, 95% Cl 0.2-0.59) and fewer drop-outs (odds ratio=0.61, 95% Cl 0.45-0.83). Duration of in-patient psychiatric treatment is shorter with community team management and costs of care are less, but there are no gains in clinical symptomatology or social functioning. CONCLUSIONS: Community mental health team management is superior to standard care in promoting greater acceptance of treatment, and may also reduce hospital admission and avoid deaths by suicide. This model of care is effective and deserves encouragement.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| 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) | 0.003 | 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".