Bibliographic record
Abstract
Recent health care policy reforms proposed by President Obama have prompted an increased interest in the efficiency of the US health care system. Looking at total health expenditure and life expectancy alone, the President has good reason to be hasty in his desire for change. Comparing the US to 18 other Organization of Economic Cooperation and Development (OECD) member countries at similar levels of development; namely: Austria, Australia, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, the Netherlands, Portugal, Spain, Switzerland, and the UK; one finds that the US has much to improve upon in these areas. In 2006, the US spent approximately 15.1% of its GDP on health care, more than any other OECD country and considerably larger than the 9.0% average of its peer nations (OECD Health Data, 2009). This is quite negatively juxtaposed with the fact that the US also has the lowest female and male life expectancies at birth of the same 18 OECD nations. The US female life expectancy at birth is 80.7 years (tied with Denmark), falling 2.1 years below the average of 82.8 years; the US male life expectancy at birth is 75.4 years, again, falling two years below the average of 77.4 years (OECD Health Data, 2009). This raw and partial evidence suggests that the US health care system may be performing inefficiently compared to its peer nations.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.077 | 0.043 |
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".