How You Pay Determines What You Get: Alternative Financing Options as a Determinant of Publicly Funded Health Care in Canada
Bibliographic record
Abstract
A Canadian returning home from a visit to a physician has no idea of the cost of providing the service just received. This is true for two reasons. One is because he or she does not receive a bill to pay. The other reason has to do the myriad of ways provincial governments fund the provision of health care. Health care is financed by a wide variety of types of taxation, by intergovernmental transfers determined by opaque and changing rules, by borrowing against future taxes and by drawing down savings. Confusion over how health care is funded creates a fiscal illusion that it is cheaper than it really is; a fiscal illusion that grows larger the less provincial governments rely on taxing individuals. In this paper it is shown that when provincial health spending is financed in ways other than taxation, it grows two to three times more quickly than it would have otherwise. From 2001-2008 alone, these distortions amounted to $6.75 billion at the national level, draining funds from other government services many of which have been shown to keep Canadians healthier and so reduce their demand for health care. Simply put, when Canadians are clear about the true cost of health care they more effectively play the traditional role of consumers by guarding against waste and inefficiency and so contribute to a more efficient and effective publicly-funded health care system.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".