Coercion and Community Treatment Orders (CTOs): One Step Forward, Two Steps Back?
Bibliographic record
Abstract
The shift from hospital-based care to community-based programs for people with serious and persistent mental illnesses has led to the creation of numerous treatment programs, including the recent implementation of community treatment orders (CTOs). This form of mandated outpatient commitment is controversial because it is widely acknowledged to be a coercive intervention. Yet, there is little discussion about why this intervention is considered coercive and whether coercion is acceptable in the context of emerging commitments to recovery for people with serious and persistent mental illnesses. Moreover, there is a need to evaluate whether CTOs advance or undermine the interests of people who are diagnosed with mental illness. This paper seeks to contribute to a discussion of these issues by exploring coercion and its role in community mental health care, and how it may co-exist with recovery in the implementation of community treatment 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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".