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Record W2264905148

Les dépenses en santé du gouvernement du Québec, 2013-2030: projections et déterminants

2013· preprint· fr· W2264905148 on OpenAlexaboutno aff
Nicholas‐James Clavet, Jean‐Yves Duclos, Bernard Fortin, Steeve Marchand, Pierre‐Carl Michaud

Bibliographic record

VenueÉrudit documents and data repository (Érudit Consortium, University of Montreal) · 2013
Typepreprint
Languagefr
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

Les données de l'Institut canadien d'information en santé (ICIS) et le modèle de micro-simulation dynamique de SIMUL permettent à la fois de projeter les dépenses en santé du Québec jusqu'en 2030 et d'en évaluer l'importance par rapport au PIB et aux revenus de l'État. Un scénario plausible prévoit que les dépenses publiques en santé augmenteront de 31,3 G$ à 61,1 G$ de 2013 à 2030, passant de 8,4 % à 13,5 % du PIB et de 42,9 % à 68,9 % des revenus totaux du gouvernement du Québec. De cette croissance de 29,8 milliards des dépenses publiques en santé, environ 12,3 G$ proviendra des effets du vieillissement de la population, 3,8 G$ sera dû à l'accroissement de la population par l'effet de l'immigration, et 18,2 G$ proviendra de la croissance des coûts structurels des soins de santé. Pour maintenir constante la part des dépenses en santé, il faudrait sous ce scénario augmenter de 60 % tous les impôts et toutes les taxes du gouvernement du Québec et ce, tout en supposant que l'assiette fiscale n'en soit pas affectée.

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)0.001
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.332
Teacher spread0.304 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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