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Record W2184863673 · doi:10.55016/ojs/sppp.v3i1.42334

Understanding the Political Economy of the Evolution and Future of Single-Payer Public Health Insurance in Canada

2010· article· en· W2184863673 on OpenAlexaffabout
J.C. Herbert Emery

Bibliographic record

VenueThe School of Public Policy Publications · 2010
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHealth careSubsidyPopulationBusinessHealth policyRevenueTyingPublic healthPublic economicsEconomic growthEconomicsFinanceEnvironmental healthMedicineMarket economy

Abstract

fetched live from OpenAlex

Surprisingly little attention has been paid to how we pay for health care affects how much we spend on health care. In this paper, I discuss how noncontributory finance and effective subsidization of public health care spending with federal cost sharing crowded out demand for private insurance as voters opted for high levels of public health spending. From this perspective, the Romanow Report’s call for increases in federal cash transfers to provinces for health care spending would result in an increase in provincial health spending and a diminution of the demand for private health insurance. It is not clear, however, that federal subsidization of health spending is either sustainable or socially desirable. Indeed, as Canada’s population ages, the current financing of health care represents enormous unfunded liabilities for the provinces. To sustain current levels and growth rates of health spending without tying current revenues to that objective means asking the next generation of working Canadians to pay far more for their health care than do working Canadians today. Although the effect of population aging on health care expenditures is projected to be modest, it could trigger a serious political crisis for Canadian medicare as taxes rise.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.131
GPT teacher head0.389
Teacher spread0.257 · 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 designObservational
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

Citations3
Published2010
Admission routes2
Has abstractyes

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