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Record W1605944529 · doi:10.1017/9781139026468.004

Private health insurance in Canada

2020· preprint· en· W1605944529 on OpenAlexaffabout
Jeremiah Hurley, G. Emmanuel Guindon

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHealth careInsurance policySelf-insuranceIncome protection insuranceHealth policyBusinessPrivate insuranceInsurance lawGeneral insuranceHealth insuranceFinanceActuarial scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

A majority of Canadians hold some form of private health care insurance, most commonly obtained as an employment benefit. Private insurance accounts for around 13% of spending on health and its financing role is essentially limited to complementary coverage for services not covered by public insurance programmes. Private supplementary insurance for services covered by the public insurance system effectively does not exist in Canada (the exception is a negligible role in the Province of Québec). This limited role for private insurance in health care reflects the core policy vision for health care financing in Canada, which emphasizes equal access to medically necessary health care, especially physician and hospital services. Compared with many other countries, Canada’s private health insurance market is relatively uncomplicated, viewed in terms of either the products offered or the regulations imposed. Although Canadians regularly debate the relative split between public and private finance overall, and a small set of advocates have persistently pressed for a greater role for private insurance, private insurance has not figured prominently in Canada’s health care policy debates, which since the late 1960s have focused on the publicly funded health care 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)0.001
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.868

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.062
GPT teacher head0.223
Teacher spread0.162 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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