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Record W3125294787 · doi:10.1215/03616878-29-3-359

How Does Private Finance Affect Public Health Care Systems? Marshaling the Evidence from OECD Nations

2004· review· en· W3125294787 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Health Politics Policy and Law · 2004
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrivate finance initiativeMarshallingPublic economicsPublic financeFinanceHealth carePrivate sectorEconomicsHarmEmpirical evidenceBusinessPolitical scienceEconomic growthMacroeconomics

Abstract

fetched live from OpenAlex

The impact of private finance on publicly funded health care systems depends on how the relationship between public and private finance is structured. This essay first reviews the experience in five nations that exemplify different ways of drawing the public/private boundary to address the particular questions raised by each model. This review is then used to interpret aggregate empirical analyses of the dynamic effects between public and private finance in OECD nations over time. Our findings suggest that while increases in the private share of health spending substitute in part for public finance (and vice versa), this is the result of a complex mix of factors having as much to do with cross-sectoral shifts as with deliberate policy decisions within sectors and that these effects are mediated by the different dynamics of distinctive national models. On balance, we argue that a resort to private finance is more likely to harm than to help publicly financed systems, although the effects will vary depending on the form of private finance.

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.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.850
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
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 head0.202
GPT teacher head0.525
Teacher spread0.323 · 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