Benefits following community treatment orders have an inverse relationship with rates of use: meta-analysis and meta-regression
Bibliographic record
Abstract
BACKGROUND: Community treatment order (CTO) use in Australia and New Zealand ranges from less than 40 per 100 000 population in Western Australia and Canterbury to over 100 per 100 000 in Victoria, South Australia and Waitemata. Recent publications on CTO use now permit a meta-regression to investigate whether differences in CTO use by jurisdiction affect either the possible predictors or outcomes of CTOs. AIMS: To assess whether factors associated with CTO placement or subsequent outcomes vary by rates of use. METHOD: A systematic search of PubMed/Medline, Embase, CINAHL, the Cochrane Central Register of Controlled Trials and PsycINFO for any Australian or New Zealand study comparing CTO cases with controls receiving voluntary psychiatric treatment. This study was prospectively registered with PROSPERO (protocol registration number: CRD42022351500). RESULTS: There were 35 articles from 16 studies identified in the search, plus unpublished data from a further study. Of these, 29 publications were included in meta-analyses. Two were from New Zealand. People who were male, single and not engaged in work, study or home duties were significantly more likely to be on CTOs. In addition, those from migrant backgrounds were 47% more likely to be on an order. On meta-regression, cases in jurisdictions with higher CTO rates had higher proportions of females or individuals with diagnoses other than non-affective psychoses. High-use jurisdictions were also less likely to show reductions in readmission rates or bed-days. CONCLUSIONS: There are marked differences in the possible predictors and outcomes of CTO placement between high- and low-use jurisdictions in Australia and New Zealand. These findings may have implications elsewhere and indicate that better-targeted CTO placement might improve 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 | 0.027 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.078 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".