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Record W3103891547 · doi:10.1503/cmaj.200143

Canadian federal–provincial/territorial funding of universal health care: fraught history, uncertain future

2020· article· en· W3103891547 on OpenAlexafffundvenueabout
C. David Naylor, Andrew Boozary, Owen Adams

Bibliographic record

VenueCanadian Medical Association Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsPublic Health OntarioCanadian Medical AssociationUniversity Health Network
FundersCanadian Medical AssociationUniversity of TorontoUniversity Health Network
KeywordsGovernment (linguistics)Coronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakHealth careKey (lock)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BusinessMedicinePublic administrationPolitical scienceEconomic growthDiseaseComputer scienceComputer securityOutbreakVirologyEconomicsInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

KEY POINTS As the coronavirus disease 2019 (COVID-19) epidemic continues in Canada and financial pressures mount on all levels of government, the federal–provincial/territorial cost-sharing framework for universal publicly financed coverage of physician and hospital services — commonly referred

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)0.019
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.169
Threshold uncertainty score0.964

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0130.012
Scholarly communication0.0150.007
Open science0.0050.007
Research integrity0.0090.015
Insufficient payload (model declined to judge)0.0220.001

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 head0.044
GPT teacher head0.362
Teacher spread0.318 · 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 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

Citations23
Published2020
Admission routes4
Has abstractyes

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