Comparing Non-Fatal Health Across Countries
Bibliographic record
Abstract
Comparing medical care systems across countries has become a preoccupation of policymakers. It is commonly asserted in the United States, for example, that the Canadian health care system is better than the US one since its per capita spending in US dollars PPP is lower, by about 45 percent, but longevity is just as high. The UK asserts its superiority over France for the same reason. Implicit in such comparisons is the idea that mortality is a good summary for the output of the medical care system. But this is not necessarily the case. Many medical services are designed not to extend life but to improve the quality of it. Indeed, entire fields of medicine – care for mental illness, ophthalmology services, physical therapy, gastroenterology, to name a few – are devoted not to extending life but to increasing its quality. And even services that were developed to extend life, such as coronary bypass surgery, are often applied in situations where quality of life more than length of life is the goal. Specialists in the field, of course, recognize the limitations of mortality for comparing health across countries. But traditionally there have been few good ways to compare morbidity across countries.1 In this paper, we propose a methodology to compare non-fatal health outcomes
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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 | 0.024 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".