An international comparison of health service use in two jurisdictions with and without outpatient commitment
Bibliographic record
Abstract
We examined whether outpatient commitment (OPC) reduces in-patient health service use for patients from Western Australia in comparison with control patients from a jurisdiction without this legislation (Nova Scotia). We used a population-based record linkage analysis of an inception cohort using a two-stage design of matching and multivariate analyses to control for socio-demographics, clinical features and psychiatric history. All discharges from inpatient psychiatric services in Western Australia and Nova Scotia were included covering a population of 2.6 million people. Patients on OPC 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 inpatient psychiatric history. We analysed time to admission using Cox-regression analyses and number of bed-days using logistic regression. We matched 196 OPC cases with an equal number of controls. On survival analyses, OPC cases had a significantly greater risk of readmission. Comorbid personality disorder and previous psychiatric history were also associated with readmission. However, on logistic regression, patients on OPC spent less time in hospital in the following year, with a reduced risk of inpatient stays exceeding 100 days. Although outpatient commitment does not reduce hospital admission rates, increased surveillance of patients on OPC 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.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".