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Entrevista com o professor Gilles Dussault: desafios dos sistemas de saúde contemporâneos, por Eleonor Minho Conill, Ligia Giovanella e José-Manuel Freire

2011· article· pt· W2433594677 on OpenAlexaboutno aff
Gilles Dussault

Bibliographic record

VenueCiência & Saúde Coletiva · 2011
Typearticle
Languagept
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Gilles Dussault é professor catedrático convidado da Unidade de Saúde Internacional e Bioestatística do Instituto de Higiene e Medicina Tropical (IHMT), Lisboa, Portugal, desde agosto de 2006. Anteriormente exerceu funções de Senior Health Specialist do Instituto do Banco Mundial, em Washington, D.C. Foi responsável pelas atividades regionais do Programa "Reforma do Sector da Saúde e Financiamento Sustentável", em diversos países de língua oficial francesa, portuguesa e espanhola, nos quais o Banco Mundial se encontrava em atividade. Seu trabalho concentrou-se no financiamento do setor saúde e em políticas de recursos humanos da saúde. Entre 1985 e 2000, assumiu funções como professor e diretor do Departamento de Administração da Saúde da Universidade de Montreal. Tem lecionado em diversos países. No Brasil, foi professor visitante na Escola Nacional de Saúde Pública Sergio Arouca (1991-92). Suas publicações concentram-se em torno de tópicos relacionados com a regulação e a gestão dos recursos humanos da saúde. Realizou diversos projetos de consultoria para agências de cooperação multilaterais e bilaterais e colabora com comitês editoriais e grupos de trabalho internacionais como a Organização Mundial da Saúde e o Observatório Europeu dos Sistemas e Políticas de Saúde.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0060.005

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 head0.079
GPT teacher head0.348
Teacher spread0.269 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2011
Admission routes1
Has abstractyes

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