Conflicting Canadian views on the value of community treatment orders: Implications for Australia and New Zealand
Bibliographic record
Abstract
Background: There is controversy as to whether community treatment orders reduce health service use, or improves clinical outcome and social functioning. Given the widespread use of these powers in North America, Australasia and Europe, this workshop assesses their benefit and potential harms.Objectives: To debate the utility of community treatment orders from the legal, clinical and research viewpoints.Method: An introduction by the presenter (SK) followed by a videotaped debate between the three authors (SK, AK, MT).Findings: There is a marked divergence of views on the effectiveness of community treatment orders. On one hand, compulsory community treatment may help to avoid still more restrictive outcomes such as arrest or imprisonment. They may improve access to treatment. People receiving compulsory community treatment are also less likely to be victims of violent or non-violent crime. However, in terms of numbers needed to treat, it would take 85 CTOs to prevent one readmission, 27 to prevent one episode of homelessness and 238 to prevent one arrest. The best evidence exists for conditional discharges, where patients are placed on an order on leaving hospital, rather than for orders made in the community. However much of the data relate to short-term outcomes and more research is needed into longer-term effects of such interventions.Conclusions: Given the similarities between Canadian and Australasian legislation, conclusions are generalisable among the constituent jurisdictions. It is important to acknowledge the limits of our knowledge and lack of consensus about community treatment orders, in spite of their widespread use.
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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 | 0.013 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".