A comparison of health service use in two jurisdictions with and without compulsory community treatment
Bibliographic record
Abstract
BACKGROUND: This study examines whether community treatment orders (CTOs) reduce psychiatric admission rates or bed-days for patients from Western Australia compared to control patients from a jurisdiction without this legislation (Nova Scotia). METHOD: A population-based record linkage analysis of an inception cohort using a two-stage design of matching and multivariate analyses to control for sociodemographics, clinical features and psychiatric history. All discharges from in-patient psychiatric services in Western Australia and Nova Scotia were included covering a population of 2.6 million people. Patients on CTOs in the first year of implementation in Western Australia were compared with controls from Nova Scotia matched on date of discharge from in-patient care, demographics, diagnosis and past in-patient psychiatric history. We analysed time to admission using Cox regression analyses and number of bed-days using logistic regression. RESULTS: We matched 196 CTO cases with an equal number of controls. On survival analyses, CTO cases had a significantly greater readmission rate. Co-morbid personality disorder and previous psychiatric history were also associated with readmission. However, on logistic regression, patients on CTOs spent less time in hospital in the following year, with reduced in-patient stays of over 100 days. CONCLUSIONS: Although compulsory community treatment does not reduce hospital admission rates, increased surveillance of patients on CTOs may lead to earlier intervention such as admission, so reducing length of hospital stay. However, we do not know if it is the intensity of treatment, or its compulsory nature, that effects outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".