What Determines Compulsory Community Treatment? A Logistic Regression Analysis Using Linked Mental Health and Offender Databases
Bibliographic record
Abstract
OBJECTIVE: Western Australia has one of the highest published rates of the use of compulsory treatment orders in the English-speaking world. Differences in patient characteristics, legislation and service setting may explain variations in the reported efficacy of compulsory community treatment. Our objective is to investigate predictors of Community Treatment Orders (CTO) placement in the first year of implementation in Western Australia and see if there were any differences in the type of patients placed on these orders compared to other studies. METHOD: A population-based record linkage study of Mental Health and Offender Databases comparing 265 patients on CTOs with a consecutive control group (CCG) of equal number matched on date of discharge from inpatient care or CTO placement. RESULTS: Previous health service use, after-care placement, mental disorder history including schizophrenic history, a positive forensic history of violence to others as well as patient's marital status were the significant predictors of CTO placement. CONCLUSIONS: Studies of compulsory community treatment appear to be of similar populations. In spite of the comparatively high rate of use, psychiatrists in Western Australia do not appear to be applying community treatment orders to different types of patient compared to elsewhere. We need further research to establish the relative contribution of patient characteristics, legislation and service setting toward the use and outcome of compulsory community treatment.
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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".