Bibliographic record
Abstract
While health system decentralization is often associated with federations, there has been limited study on the connection between federalism and the organization of publicly financed or mandated health services.Federalism and Decentralization in Health Care discusses eight federations that differ in terms of their geography, history, and constitutional and political development.Looking at Canada, Brazil, Germany, Mexico, Nigeria, Pakistan, South Africa, and Switzerland, this volume examines constitutional responsibility for health care, the national laws establishing a right to health care, the predominant sources and organization of public revenues directed to health care, and the overall organization of the health system.In additional to these structural features, each country case study is subjected to a "decision space analysis" to determine the actual degree of health system decentralization.This involves determining whether national and subnational governments have narrow, moderate, or broad discretion in their decisions on health system governance, access, human resources, organization, and financing.This comparative approach highlights similarities and differences among these federations.Offering reflections on recent trends in centralization or decentralization for the health system as a whole, Federalism and Decentralization in Health Care is a valuable resource for those studying health care policy in federal systems and especially those interested in comparative aspects of the topic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.870 | 0.787 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".