Federalismo y asistencia sanitaria: distribución de competencias y fianciación en Estados Unidos, Canadá y Alemania
Bibliographic record
Abstract
The aim of this paper is to analyze the decisión-making and financial repercussions that the introduction of healthcare has had in the federal states. For this, a historical analysis is made of three states with a long federal tradition, but of a different health system: USA, Canada and Germany. It will not be discussed here which of them is more efficient for the health coverage of citizens, but their competencies (what level of government assumes decisions) and financial (what level of government pays them), as well as the congruence between the two. From this analysis it can be seen that the incorporation of health care in federal structures meant not only a centralization of competencies, but also a restructuring of funding sources and spending. On the other hand, it is also observed that the financial capacity of the center and of the territories is what will determine its decision-making capacity, above, even, of the formal system of competence distribution. In fact, conflicts between the center and the territories in the comparative experience are born, not when it is decided outside the competence system, but when it is decided, but without granting sufficient resources. Hence, the configuration of the financing system of the territorial units of a federal State is fundamental for the distribution of competences in healthcare in practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".