US spends the most but doesn't get the best results, a comparison of health data shows
Bibliographic record
Abstract
A multinational comparison of health systems data has found that the United States spends more on health care than other countries but often doesn’t get the best results; the United Kingdom comes out in the middle on most measures. Drs Gerard Anderson and Patricia Marcovich of Johns Hopkins University did the study with funding from the Commonwealth Fund, a non-profit foundation in New York that seeks to improve health care. They compared data on healthcare spending and outcomes from nine industrialised countries (the US, Switzerland, Canada, France, the Netherlands, Germany, Australia, the UK, and New Zealand) with the median for the 30 countries in the Organization for Economic Co-operation and Development (OECD). The study compares data between 1996 and 2006, the latest year for which data are available. In 2006, healthcare spending per capita in the US was double that for OECD countries and the UK ($6714 (£4400; €5000), $2880, …
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".