Lessons for the United States From Single-Payer Systems
Bibliographic record
Abstract
US political debates often refer to the experience of "single-payer" systems such as those of Canada and the United Kingdom. We argue that single payer is not a very useful category in comparative health policy analysis but that the experiences of countries such as Canada, the United Kingdom, Spain, Sweden, and Australia provide useful lessons. In creating universal tax-financed systems, they teach the importance of strong, unified governments at critical junctures-most notably democratization. The United States seems politically hospitable to creating such a system.The process of creation, however, highlights the malleability of interests in the health care system, the opportunities for creative coalition building, and the problems caused by linking health care finance and reform. In maintaining these systems, keeping the middle class supportive is crucial to avoiding universal health care that is essentially a program for the poor.For a technical term from the 1970s, "single-payer health care" has proved to have remarkable political power and persistence. We argue it is not a very useful term but the lessons from such systems can be valuable for those contemplating movement toward universal health coverage in the United States.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".