Advocacy Coalitions and Mental Health Policy: The Adoption of Community Treatment Orders in<scp>O</scp>ntario
Bibliographic record
Abstract
InCanada, the provincial level of government is primarily responsible for the provision of mental health‐care services. In 2000, theOntario government introduced community treatment orders (CTOs) as a new instrument for treating the mentally ill.CTOswere more coercive than prevailing practices, allowing mentally ill individuals to be compelled to receive treatment for their mental illness, including pharmacological treatment, on an outpatient basis. Using the advocacy coalition framework, this article explains the introduction ofCTOsby identifying the prevailing advocacy coalitions in theOntario mental health policy subsystem and by examining the power resources available to them in their efforts to influence policy decision makers. Ultimately, the pro‐CTOcoalition was successful because it had public opinion, information, and credibility advantages that the anti‐CTOcoalition simply could not match. Related Articles McGrath , Robert J. 2009 . “” Politics & Policy 37 (): 309 ‐ 336 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2009.00174.x/abstract Barnes , Nielan . 2011 . “” Politics & Policy 39 (): 69 ‐ 89 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2010.00283.x/abstract Morris , Mary Hallock . 2007 . “” Politics & Policy 35 (): 836 ‐ 871 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2007.00086.x/abstract Related Media . 2013 . “” April 18. http://www.youtube.com/watch?v=r5v77FqydYk . 2000 . “ ” May 24. http://www.cbc.ca/news/canada/brian‐s‐law‐gets‐hearing‐in‐ottawa‐1.232466 Paikin , Steve . 2011 . “” [video file]. January 10. http://www.youtube.com/watch?v=dzOk_Qdjo48
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.030 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".