Overcoming challenges in health-care reform
Bibliographic record
Abstract
Canada’s public health-care system (Medicare) offers top-notch care for legislatively-defined essential services, without charge, to all residents. Treatment quality and aggregate health outcomes are good, and the population values in particular the notion of fairness. However, pressure for health-care cost control is constant. Allocations of scarce health-care resources take place through supply-side restrictions, often in the form of waiting lists for elective services, as prices do not play any role. As a result of the historical development of Canada’s health care system, some of the fastest rising costs, notably for pharmaceuticals and home care, are largely outside of Medicare. Despite equality of access to health care, there is some inequality of health outcomes, suggesting the need to pay greater attention to other (social) determinants of health. As in other OECD countries, constraints are set to tighten further, both in the medium term as post-crisis deficits are wound down, and more durably in the longer term, as an ageing population requires substantially more services, while growth in the tax base to fund them slows, and technology goes on expanding possibilities for life extension and life quality. The objective should be to complement top-down supply control by more accountability and use of price incentives at the micro level as the main means of promoting both efficiency and quality, and hence system sustainability.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".