Health Status, Health Care and Inequality: Canada vs. the U.S.
Bibliographic record
Abstract
Does Canada's publicly funded, single payer health care system deliver better health outcomes and distribute health resources more equitably than the multi-payer heavily private U.S. system? We show that the efficacy of health care systems cannot be usefully evaluated by comparisons of infant mortality and life expectancy. We analyze several alternative measures of health status using JCUSH (The Joint Canada/U.S. Survey of Health) and other surveys. We find a somewhat higher incidence of chronic health conditions in the U.S. than in Canada but somewhat greater U.S. access to treatment for these conditions. Moreover, a significantly higher percentage of U.S. women and men are screened for major forms of cancer. Although health status, measured in various ways is similar in both countries, mortality/incidence ratios for various cancers tend to be higher in Canada. The need to ration resources in Canada, where care is delivered "free", ultimately leads to long waits. In the U.S., costs are more often a source of unmet needs. We also find that Canada has no more abolished the tendency for health status to improve with income than have other countries. Indeed, the health-income gradient is slightly steeper in Canada than it is in the U.S.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".