Understanding the Political Economy of the Evolution and Future of Single-Payer Public Health Insurance in Canada
Bibliographic record
Abstract
Surprisingly little attention has been paid to how we pay for health care affects how much we spend on health care. In this paper, I discuss how noncontributory finance and effective subsidization of public health care spending with federal cost sharing crowded out demand for private insurance as voters opted for high levels of public health spending. From this perspective, the Romanow Report’s call for increases in federal cash transfers to provinces for health care spending would result in an increase in provincial health spending and a diminution of the demand for private health insurance. It is not clear, however, that federal subsidization of health spending is either sustainable or socially desirable. Indeed, as Canada’s population ages, the current financing of health care represents enormous unfunded liabilities for the provinces. To sustain current levels and growth rates of health spending without tying current revenues to that objective means asking the next generation of working Canadians to pay far more for their health care than do working Canadians today. Although the effect of population aging on health care expenditures is projected to be modest, it could trigger a serious political crisis for Canadian medicare as taxes rise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".