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Record W2042107497 · doi:10.3917/reco.602.0545

Pourquoi les systèmes de santé sont-ils organisés différemment ?

2009· article· fr· W2042107497 on OpenAlexaff
Michel Grignon

Bibliographic record

VenueRevue économique · 2009
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Résumé Un système national de santé organise et régule simultanément deux types d’interventions publiques : tout d’abord, comme toute politique sociale, il met en œuvre des transferts de revenus, entre ménages, selon la richesse et l’état de santé, mais aussi entre ménages et producteurs de soins. Il agit aussi comme régulateur des relations entre des producteurs et des consommateurs d’un bien essentiellement privé mais difficilement observable, la santé. On peut donc concevoir que ces systèmes soient organisés très différemment d’un pays à l’autre, et chercher quels sont les facteurs, objectifs comme le degré d’inégalité des revenus, ou liés aux valeurs, comme l’aversion pour l’inégalité, qui expliquent ces différences entre systèmes nationaux de santé. Cet article est une première étape sur la voie d’une modélisation de la façon dont les systèmes nationaux font des choix : les modèles théoriques sont passés en revue, ainsi que les variables dépendantes à retenir pour décrire les systèmes de santé.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.039
GPT teacher head0.243
Teacher spread0.204 · 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.

Study designNot applicable
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

Citations7
Published2009
Admission routes1
Has abstractyes

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