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Record W4253916095 · doi:10.1215/03616878-2008-012

The Form and Context of Federalism: Meanings for Health Care Financing

2008· article· en· W4253916095 on OpenAlexaboutno aff
George France

Bibliographic record

VenueJournal of Health Politics Policy and Law · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsFederalismContext (archaeology)Cooperative federalismHealth carePublic administrationPolitical scienceMeaning (existential)New FederalismDual federalismPublic economicsEconomicsLawPsychologyPoliticsGeography

Abstract

fetched live from OpenAlex

This article examines the meaning of federalism for health care financing (HCF) and is based on two considerations. First, federal institutions are embedded in their national context and interact with them. The design and performance of HCF policy will be influenced by contexts, the workings of the federal institutions, and the interactions of these institutions with different elements of the context. This article unravels these influences. Second, there is no unique model of federalism, and so we have to specify the particular form to which we refer. The examination of the influence of federalism and its context on HCF policy is facilitated by using a transnational comparative approach, and this article examines four mature federations: the United States, Australia, Canada, and Germany. The relatively poor performance of the U.S. HCF system seems associated with the fact that it operates in a context markedly less benign than those of the other national HCF systems. Heterogeneity of context appears also to have contributed to important differences between the United States and the other countries in the design of HCF policies. An analysis of how federalism works in practice suggests that, while U.S. federalism may be overall less favorable to the development of well-functioning HCF policies, the inferior performance of these policies is to be principally attributed to context.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.336
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2008
Admission routes1
Has abstractyes

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