Mental Health Service Delivery in Ontario, Canada: How Do Policy Legacies Shape Prospects for Reform?
Bibliographic record
Abstract
Like many jurisdictions, mental health policy-making in Ontario, Canada, has a long history of frustrated attempts to move from a hospital and physician-based tradition to a coordinated system with greater emphasis on community-based mental health care. This study examines policy legacies associated with the introduction of psychiatric hospitals in the 1850s and of public health insurance (medicare) in the 1960s in Ontario; and their effect on subsequent mental health reform initiatives using a qualitative case study approach. Following Pierson (1993) we capture the resource/incentive and interpretive effects of prior policies on three groups of actors: government elites, interests, and mass publics. Data are drawn from academic and policy literature, and key informant interviews. The findings suggest that psychiatric hospital policy produced important policy legacies which were reinforced by the establishment of Canadian medicare. These legacies explain the traditional difficulty in achieving mental health reform, but are less helpful in explaining recent promising developments that support community-based care. Current reform of the Ontario health system presents an opportunity to overcome several of these legacies. Analysis of policy legacies in other countries which had an asylum tradition may help to explain the similarities and differences in their subsequent paths of mental health reform.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".