Steering and Rowing in Health Care: The Devolution Option?
Bibliographic record
Abstract
Publicly funded health care systems are often the subject of heated policy debates. All too often (particularly in Canada), these debates focus on the prohibitive costs, the resultant taxation levels, and the questionable efficiency and outcomes associated with a publicly funded system. Moreover, the institutionalization of the system and the entrenchment of its many stakeholders make effecting change particularly difficult. In this article, the authors begin with an assessment of the drawbacks of the Canadian health care system in the federal-provincial context and its resulting gaps in governance (steering), in management (rowing), and in overall accountability (apart from that offered by periodic elections). They conclude that because of the above factors and the public's personal investment in a perceived health care entitlement, policymakers tend to avoid long-term changes to the system in favour of such short-term solutions as increased funding or reductions in funding services. If fixing health care is the goal, governments must embrace more fundamental change. Devolution is a process through which governments give up their hold on the detailed management of the health care system, in exchange for more involvement in its governance. In this way, it allowed the government to steer - by defining values, setting goals and objectives, and evaluating outcomes and efficiencies of the system - while allowing those that deliver health care to determine how best to do so (in other words, allowing the rowers to row). In light of the successes and failures of devolution in other jurisdictions, the authors then present a model for devolution that they argue will allow the Canadian health care system to flourish. Although devolution is only one part of the solution, it is a fundamental part.
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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 | 0.060 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.064 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".