Restricting community treatment orders to people with non-affective psychosis is needed to reduce use and improve subsequent outcomes: Queensland-wide cohort study
Bibliographic record
Abstract
BACKGROUND: The use of community treatment orders (CTOs) has increased in many jurisdictions despite very limited evidence for their efficacy. In this context, it is important to investigate any differences in outcome by subgroup. AIMS: To investigate the variables associated with CTO placement and the impact of CTOs on admissions and bed-days over the following 12 months, including differences by diagnosis. METHOD: Cases and controls from a complete jurisdiction, the state of Queensland, Australia, were analysed. Administrative health data were matched by age, sex and time of hospital discharge (index date) with two controls per case subject to a CTO. Multivariate analyses were used to examine factors associated with CTOs, as well as the impact on admissions and bed-days over the 12 months after CTO placement. Registration: Australian and New Zealand Clinical Trials Registry (ACTRN12624000152527). RESULTS: We identified 10 872 cases and 21 710 controls from January 2018 to December 2022 (total n = 32 582). CTO use was more likely in First Nations people (adjusted odds ratio = 1.14; 95% CI: 1.06-1.23), people from culturally diverse backgrounds (adjusted odds ratio = 1.45; 95% CI: 1.33-1.59) and those with a preferred language other than English (adjusted odds ratio = 1.21; 95% CI: 1.02-1.44). When all diagnostic groups were considered, there were no differences in subsequent admissions or bed-days between cases and controls. However, both re-admissions and bed-days were significantly reduced for CTO cases compared with controls in analyses restricted to non-affective psychoses (e.g. adjusted odds ratio = 0.77, 95% CI: 0.71-0.84 for re-admission). CONCLUSIONS: Queenslanders from culturally or linguistically diverse backgrounds and First Nations peoples are more likely to be placed on CTOs. Targeting CTO use to people with non-affective psychosis would both address rising CTO rates and mean that people placed on these orders derive possible benefit. This has implications for both clinical practice and policy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".