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Record W2076728409 · doi:10.1215/03616878-2009-011

Single Payers, Multiple Systems: The Scope and Limits of Subnational Variation Under a Federal Health Policy Framework

2009· article· en· W2076728409 on OpenAlexaffabout
Carolyn Hughes Tuohy

Bibliographic record

VenueJournal of Health Politics Policy and Law · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHealth careScope (computer science)Government (linguistics)Public administrationCorporate governancePoliticsBusinessPublic economicsPolitical scienceEconomicsFinanceLaw

Abstract

fetched live from OpenAlex

In political discourse, the term "single-payer system" originated in an attempt to stake out a middle ground between the public and private sectors in providing universal access to health care. In this view, a single-payer system is one in which health care is financed by government and delivered by privately owned and operated health care providers. The term appears to have been coined in U.S. policy debates to provide a rhetorical reference point for universal health insurance other than the "socialized medicine" of state-owned and -operated health care providers. This article, like others in this special issue, is meant to provide a more nuanced view of single-payer systems. In particular, it reviews experience in the prototypical single-payer system for physician and hospital services: the Canadian case. Given Canada's federal governance structure, this example also aptly illuminates the scope and limits of subnational variation within this single model of health care finance. And what it demonstrates in essence is that the very feature that defines the single-payer prototype -- the maintenance of independent providers remunerated by a single public payer in each province -- also leads to a set of profession-state bargains that define the limits of variation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.344
Teacher spread0.258 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations30
Published2009
Admission routes2
Has abstractyes

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