Comparative health systems: global perspectives.
Bibliographic record
Abstract
In an increasingly globalized society, a clear understanding of international healthcare systems is a fundamental step toward improving the quality of health and healthcare systems in the United States and abroad. Comparative Health Systems: Global Perspectives explores the health systems of 17 countries through comparative study. Using a consistent framework, the chapters offer an overview of each country's history, geography, government, and economy, as well as a detailed analysis of the country's healthcare system facilities, workforce, technology, cost, quality, and access. Current and emerging issues are also explored. The book concludes with a look at the changing U.S. healthcare system and the global challenges and opportunities for health. Ideal for courses in global health, international affairs, health administration, and public health, this innovative text challenges its readers to reflect deeply about how health care is organized and delivered. Key Features: Each chapter follows a consistent framework to allow the reader to easily compare and contrast the divergent systems of various countries. Chapter contributors are scholars and experts from countries including Canada, India, Japan, Nigeria, Germany, Australia, Mexico, the United States, and more. A forthcoming companion website will feature interactive student resources to further enhance learning as well as resources for the instructor including an Instructor's Manual, Test Bank, PowerPoint slides.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".