Community Treatment Orders: Ethical Practice in an Era of Magical Thinking
Bibliographic record
Abstract
With the passage of legislation in June 2000, coercive measures in the form of community treatment orders (CTOs) have become part of the community mental health landscape in Ontario. Given that community practitioners place a high value upon their ability to create voluntary and egalitarian partnerships with clients, the question of whether ethical practice is possible under conditions of legislated coercion is relevant. Based upon a review of the pro and con arguments that preceded CTO legislation, followed by an examination of available research on effectiveness, this paper suggests that forms of magical thinking have been at work on both sides of the CTO debate. A broader definition of coercion is proposed--one that envelopes both overt and covert forms. Finally, the author offers an approach to ethical practice which is based on the use of transformative power rather than coercive power, and which includes a 3-step strategy (using liberation tactics, proactive contracting, and procedural justice).
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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 | 0.049 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.175 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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".