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Record W3088361627 · doi:10.3917/spub.202.0211

Le difficile pilotage d’une réforme d’un système de santé : cas du Nouveau-Brunswick (Canada)

2020· article· fr· W3088361627 on OpenAlexaffabout
Stéphanie Collin, Lise Lamothe

Bibliographic record

VenueSanté Publique · 2020
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalUniversité de Moncton
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

INTRODUCTION: In many developed countries, reforms of public healthcare systems are ongoing but do not always achieve desired results. In this article, we present the history of the healthcare system reform in the Canadian province of New Brunswick with the objective of analyzing its difficult steering by the state, in light of the dynamics between the actors involved. METHOD: Qualitative methods were chosen. Data collection includes semi-structured interviews (N = 39) with representatives of the State, such as health ministers, and other relevant stakeholders, such as managers, citizens or health professionals. RESULTS: The stakeholders were compelled by various aspects of the reform, for example francophone health care services, that had consequences on the trajectory of change. To stay on target, the State must adapt to the dynamic interactions of the actors involved. CONCLUSION: Reforms take place over a long period of time and their programming by the State can be very difficult, as it requires the mobilization of different types of instruments at its disposal. In order to influence the behaviour of the actors concerned, the State must define a goal whose general orientations are agreed upon, succeed in forging bonds of trust and managing resistance, and finally, use standardized data in order to provide a normative framework and evaluate the progress of the reform project.

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)0.004
metaresearch head score (Gemma)0.005
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 categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.336
Teacher spread0.307 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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