EXPANDING ACCESS TO PUBLICLY-FUNDED PSYCHOTHERAPY: A COMPARATIVE POLICY ANALYSIS IN ONTARIO AND BRITISH COLUMBIA
Bibliographic record
Abstract
In 2021, Ontario became the first province in Canada to adopt a provincial publicly funded psychotherapy program, after a three-year long pilot project (2017-2020). Meanwhile, more than a decade prior, in 2008, BC introduced a smaller-scale program, Bounce Back, but opted to not adopt a larger-scale provincial program. This dissertation investigates the development of publicly funded psychotherapy in Ontario and BC through a comparative policy analysis of mental health policy reform. Broadly, this dissertation asks: “Under what conditions do jurisdictions achieve mental health reform?” More specifically, it addresses two sub-questions: 1) What conditions allowed BC to adopt an early, low intensity program and then constrained it from adopting a larger-scale program? 2) What conditions limited early policy development in Ontario, but allowed for a more ambitious and larger-scale program more recently? This dissertation draws on four well-established theoretical frameworks in political science: ideas, institutions, policy learning, and the role of policy entrepreneurs, to analyze and explain Ontario and BC’s divergent reform trajectories. Empirically, the research draws on extensive qualitative methods, including documentary analysis and over 30 elite interviews with policymakers, healthcare leaders and providers. These interviews offer insider perspectives on the motivations, challenges, and strategic decisions behind the policy decisions each province made. The findings reveal key differences in the policy processes and contextual factors that led Ontario to adopt a UK-inspired provincial model through the Ontario Structured Psychotherapy Program, in contrast to BC’s decision to implement a smaller-scale, low-intensity program, Bounce Back.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".