A systematic review and meta-analysis of predictors and outcomes of community treatment orders in Australia and New Zealand
Bibliographic record
Abstract
OBJECTIVES: Australia and New Zealand have some of the highest rates of compulsory community treatment order use worldwide. There are also concerns that people from culturally and linguistically diverse backgrounds may have higher rates of community treatment orders. We therefore assessed the health service, clinical and psychosocial outcomes of compulsory community treatment and explored if culturally and linguistically diverse, indigenous status or other factors predicted community treatment orders. METHODS: We searched the following databases from inception to January 2020: PubMed/Medline, Embase, CINAHL and PsycINFO. We included any study conducted in Australia or New Zealand that compared people on community treatment orders for severe mental illness with controls receiving voluntary psychiatric treatment. Two reviewers independently extracted data, assessing study quality using Joanna Briggs Institute scales. RESULTS: A total of 31 publications from 12 studies met inclusion criteria, of which 24 publications could be included in a meta-analysis. Only one was from New Zealand. People who were male, single and not engaged in work, study or home duties were significantly more likely to be subject to a community treatment order. In addition, those from a culturally and linguistically diverse or migrant background were nearly 40% more likely to be on an order. Indigenous status was not associated with community treatment order use in Australia and there were no New Zealand data. Community treatment orders did not reduce readmission rates or bed-days at 12-month follow-up. There was evidence of increased benefit in the longer-term but only after a minimum of 2 years of use. Finally, people on community treatment orders had a lower mortality rate, possibly related to increased community contacts. CONCLUSION: People from culturally and linguistically diverse or migrant backgrounds are more likely to be placed on a community treatment order. However, the evidence for effectiveness remains inconclusive and limited to orders of at least 2 years' duration. The restrictive nature of community treatment orders may not be outweighed by the inconclusive evidence for beneficial outcomes.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".