Variations between, and within, jurisdictions in the use of community treatment orders and other compulsory community treatment: study of 402 060 people across four Australian states
Bibliographic record
Abstract
BACKGROUND: The use of compulsory community treatment (CCT) in Australia is some of the highest worldwide despite limited evidence of effectiveness. Even within Australia, use varies widely across jurisdictions despite general similarities in legislation and health services. However, there is much less information on whether variation occurs within the same jurisdiction. AIMS: To measure variations in the use of CCT in a standardised way across the following four Australian jurisdictions: Queensland, South Australia, New South Wales (NSW) and Victoria. We also investigated associated sociodemographic variables. METHODS: We used aggregated administrative data from the Australian Institute of Health and Welfare. RESULTS: There were data on 402 060 individuals who were in contact with specialist mental health services, of whom 51 351 (12.8%) were receiving CCT. Percentages varied from 8% in NSW to 17.6% in South Australia. There were also wide variations within jurisdictions. In NSW, prevalence ranged from 2% to 13%, in Victoria from 6% to 24%, in Queensland from 11% to 25% and in South Australia from 6% to 36%. People in contact with services who were male, single and aged between 25 and 44 years old were significantly more likely to be subject to CCT, as were people living in metropolitan areas or those born outside Oceania. CONCLUSIONS: There are marked variations in the use of CCT both within and between Australian jurisdictions. It is unclear how much of this variation is determined by clinical need and these findings may be of relevance to jurisdictions with similar clinician-initiated orders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".