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Medical Doctors in Health Reforms

2022· book· en· W4297132523 on OpenAlexaboutno aff
Jean‐Louis Denis, Sabrina Germain, Catherine Régis, Gianluca Veronesi

Bibliographic record

VenuePolicy Press eBooks · 2022
Typebook
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsUnderpinningHealth careContext (archaeology)Government (linguistics)PoliticsPolitical sciencePublic relationsHealth policyHealth care reformNarrativePublic administrationLawEngineering

Abstract

fetched live from OpenAlex

Medical doctors play a crucial role in the allocation and use of resources in health care systems. They shape capacities to renew policy orientations and innovate models of care. However, little attention has been paid to their specific role in health reforms. This book explores this aspect by looking at the role of the medical profession in health reforms in two mature welfare states with publicly-funded healthcare systems (PFHS): Canada and England. Specifically, the book investigates the multifaceted and paradoxical situation where a dominant profession – medicine – faces increasing pressures to become an active player and an ally in major policy efforts and system-wide reforms driven by governments. The conceptual underpinning of this work builds on the contribution of various areas of studies, namely the sociology of professions, studies on professions and organisations and law. The analysis investigates reformative processes from the inception of both PFHS and identifies the role of the medical profession in policy formulation. The focus is predominantly on the role of organised medicine (unions, professional associations and colleges) with their political struggles to promote and advance medical values and interests in a context where governments aim to transform health care systems. Empirically, the book builds on a socio-historical and institutional narrative of health care reforms and on the role played by the medical profession in both countries. The book offers insights into the government's ability to drive change in the health care system and to engage medical doctors as partners in health reforms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.014
Scholarly communication0.0080.007
Open science0.0010.004
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0130.003

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 head0.104
GPT teacher head0.479
Teacher spread0.375 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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