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Record W4408690817 · doi:10.1192/bjo.2025.23

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

2025· article· en· W4408690817 on OpenAlexaff
Steve Kisely, Claudia Bull, Tessa‐May Zirnsak, Vrinda Edan, Morgan Gould, Sharon Lawn, Edwina Light, Chris Maylea, Giles Newton‐Howes, Christopher Ryan, Penelope Weller, Lisa Brophy

Bibliographic record

VenueBJPsych Open · 2025
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsDalhousie University
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsJurisdictionMetropolitan areaLegislationWelfareGeographyMedicineDemographyPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.332
GPT teacher head0.498
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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