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Record W2122724260 · doi:10.1002/hec.1698

Public and private health‐care financing with alternate public rationing rules

2010· article· en· W2122724260 on OpenAlexafffund
Katharine Cuff, Jeremiah Hurley, Stuart Mestelman, Andrew Muller, Robert Nuscheler

Bibliographic record

VenueHealth Economics · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsHealth Sciences CentreMcMaster University
FundersCanadian Institutes of Health ResearchMcMaster University
KeywordsRationingPrivate sectorBusinessPublic sectorActuarial scienceHealth carePublic economicsHealth care rationingPublic healthPrivate insuranceHealth insuranceSelf-insuranceEconomicsFinanceEconomic growthMedicineNursing

Abstract

fetched live from OpenAlex

We develop a model to analyze parallel public and private health-care financing under two alternative public sector rationing rules: needs-based rationing and random rationing. Individuals vary in income and severity of illness. There is a limited supply of health-care resources used to treat individuals, causing some individuals to go untreated. Insurers (both public and private) must bid to obtain the necessary health-care resources to treat their beneficiaries. Given individuals' willingnesses-to-pay for private insurance are increasing in income, the introduction of private insurance diverts treatment from relatively poor to relatively rich individuals. Further, the impact of introducing parallel private insurance depends on the rationing mechanism in the public sector. We show that the private health insurance market is smaller when the public sector rations according to need than when allocation is random.

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)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0050.005
Open science0.0030.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.075
GPT teacher head0.273
Teacher spread0.197 · 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 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

Citations21
Published2010
Admission routes2
Has abstractyes

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