Is the Effect of Compulsory Community Treatment on Preventable Deaths from Physical Disorders Mediated by Better Access to Specialized Medical Procedures?
Bibliographic record
Abstract
OBJECTIVES: Compulsory community treatment has been shown to reduce preventable deaths from physical disorders-these causes being up to 10 times more common than suicide in psychiatric patients. We investigated whether this was mediated by better access to specialized medical procedures. METHOD: All patients on compulsory community treatment for over 11 years were compared with matched control subjects using linked administrative health data from Western Australia (state population of about 2.24 million). Outcomes were access to revascularization and other specialized procedures at 1-, 2-, and 3-year follow-up. Logistic regression was used to adjust for demographics, prior health service use, diagnosis, and length of psychiatric history. RESULTS: There were 2757 patients and 2687 control subjects (total n = 5444). Sixty-five per cent were males (n = 3522), and the average age was 36 years (SD 13.2). Most had schizophrenia or other nonaffective psychoses (74%), followed by affective disorders (26%). At 2-year follow-up, 2% (n = 53) of patients and 2.6% (n = 69) of control subjects had undergone a specialized intervention. Compulsory community treatment did not result in greater access to specialized procedures at all 3 time points even after adjusting for potential confounders. CONCLUSIONS: Greater access to specialized procedures does not explain the reduced mortality from preventable physical illness that had been reported in patients on community treatment orders. There must be other explanations for this finding, such as mental health staff facilitating access to chronic disease management in primary care. This warrants further research.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".