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Record W3134201780 · doi:10.12927/hcpol.2021.26435

Increased Private Healthcare for Canada: Is That the Right Solution?

2021· article· en· W3134201780 on OpenAlexafffundvenueabout
Shoo K. Lee, Brian H. Rowe, Sukhy Mahl

Bibliographic record

VenueHealthcare policy · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of AlbertaMount Sinai Hospital
FundersCanadian Institutes of Health Research
KeywordsHealth careBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

Medicare is a publicly funded healthcare system that is a source of national pride in Canada; however, Canadians are increasingly concerned about its performance and sustainability. One proposed solution is private financing (including both private for-profit insurance and private out-of-pocket financing) that would fundamentally change medicare. We investigate international experiences to determine if associations exist between the degree of private spending and two of the core values of medicare - universality and accessibility - as well as the values of equity and quality. We further investigate the impact of private spending on overall health system performance, health outcomes and health expenditure growth rates. Private financing (both private for-profit insurance and private out-of-pocket financing) was found to negatively affect universality, equity, accessibility and quality of care. Increased private financing was not associated with improved health outcomes, nor did it reduce health expenditure growth. Therefore, increased private financing is not the panacea proposed for improving quality or sustainability. The debate over the future of medicare should not be rooted in the source of its funding but rather in the values Canadians deem essential for their healthcare system.

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)0.024
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: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.914
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0080.009
Scholarly communication0.0110.007
Open science0.0020.003
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0130.001

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 head0.068
GPT teacher head0.303
Teacher spread0.235 · 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
GenreCommentary

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

Citations24
Published2021
Admission routes4
Has abstractyes

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