Policy Capacity for Health Reform: Necessary but Insufficient Comment on "Health Reform Requires Policy Capacity"
Bibliographic record
Abstract
Forest and colleagues have persuasively made the case that policy capacity is a fundamental prerequisite to health reform. They offer a comprehensive life-cycle definition of policy capacity and stress that it involves much more than problem identification and option development. I would like to offer a Canadian perspective. If we define health reform as re-orienting the health system from acute care to prevention and chronic disease management the consensus is that Canada has been unsuccessful in achieving a major transformation of our 14 health systems (one for each province and territory plus the federal government). I argue that 3 additional things are essential to build health policy capacity in a healthcare federation such as Canada: (a) A means of "policy governance" that would promote an approach to cooperative federalism in the health arena; (b) The ability to overcome the "policy inertia" resulting from how Canadian Medicare was implemented and subsequently interpreted; and (c) The ability to entertain a long-range thinking and planning horizon. My assessment indicates that Canada falls short on each of these items, and the prospects for achieving them are not bright. However, hope springs eternal and it will be interesting to see if the July, 2015 report of the Advisory Panel on Healthcare Innovation manages to galvanize national attention and stimulate concerted action.
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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 | 0.034 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.032 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.115 | 0.103 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".