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Record W2606482399 · doi:10.7202/1038889ar

Roemer 20 Years Later: When a Classical Health-System Typology Meets Market-Oriented Reforms

2017· article· en· W2606482399 on OpenAlexvenueno aff
Mélanie Bourque, Jean-Simon Farrah

Bibliographic record

VenueRevue Gouvernance · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsTypologyCorporate governanceHealth careHealth care reformCompetition (biology)Health policyManaged careEconomicsPublic economicsPolitical scienceSociologyEconomic growthManagement

Abstract

fetched live from OpenAlex

In 1990, Roemer came up with a very influential health system typology. From his vast study, emerged three types of health care systems: nationalized, mandated and entrepreneurial. Health care systems are not static; slow changes and reforms somewhat alter values and goals on which those systems were initially established. It is fair to say, then, that over the last two decades, health care reformers have adopted a market-oriented governance model that blends new public management (NPM) and managed competition reforms in the provision of health care services to transform supply- and demand-side actors into “responsibilized” customers, payers or providers. These transformations beg the question as to whether we are witnessing a radical redefinition of health care systems through the implementation of market-oriented governance. We propose to add the evolution of market-oriented health reforms in five case studies to Milton Roemer’s typology of health systems. In light of our findings, we will wrap up the analysis with an assessment of the usefulness of Roemer’s classification for social scientists to grasp the evolution of health systems over the past 20 years, and more importantly, to analyze the current state of these health care systems after years of market-oriented 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: none
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.041
GPT teacher head0.269
Teacher spread0.229 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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