Latest findings highlight the continuing uncertainty over the utility of compulsory psychiatric treatment in the community
Bibliographic record
Abstract
Community treatment orders (CTOs) have been introduced in many jurisdictions with evidence of increasing use over time as well as a disproportionate use in marginalised populations. Rates of CTOs also vary widely, both internationally and within the same country, for reasons that are poorly understood. This is despite evidence for effectiveness being mixed and, as a result, there have been calls for a reappraisal of this type of legislation. In the UK, a parliamentary committee on reforming the existing Mental Health Act recommended abolishing CTOs other than for people in the criminal justice system. Two recent Australian papers based on large state-wide administrative data-sets give conflicting results and came to markedly different conclusions regarding the desirability of reducing CTO rates. The debate about the effectiveness of CTOs therefore remains unresolved. This is of concern beyond Australia, as other jurisdictions such as England, Scotland and Canada have 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 | 0.023 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".