Health-Care Reform in Ontario: More Tortoise Than Hare?
Bibliographic record
Abstract
If the province of Ontario were one of the two characters in the fable about the tortoise and the hare and the race were health-care reform, Ontario would more closely resemble the tortoise during the 1990-2003 period.Whereas other provinces acted almost spritely when they regionalized health services delivery, Ontario held back, acting as the "control group" for such ambitious reforms.Ontario did occasionally pick up a bit of speed in its efforts to ensure timely access to high-quality health care for all citizens or the system's financial and political sustainability.The province established both a wait-list tracking system (the Cardiac Care Network) and a new prescription drug plan (the Trillium Drug Program).But it is in the domain of the core "public payment/private delivery" bargains where Ontario's slow and steady approach might yet win the race for most significant reforms (Lavis 2004).The province prepared the groundwork for the private for-profit delivery of medically necessary services (although this yielded fairly small impacts) and, more notably, established an alternative payment plan for primary care physicians that has attracted ever more physicians away from the traditional fee-for-service payment plan (Hutchison et al. 2011; Kralj and Kantarevic 2012).While it is far from clear that changing the core bargains with hospitals and physicians in Ontario will translate into timely access to high-quality health care, better health outcomes, or greater financial and political sustainability for the system, structural reforms were the holy grail of the period and were being called for by many commissions and task forces, both provincially and nationally (National Forum on Health 1997a; Ontario Health Services Restructuring Commission 1999).Ontario's reforms were not comprehensive, however, even though their effect over
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".