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Record W3124077486

Rethinking Canada’s Unbalanced Mix of Public and Private Healthcare: Insights from Abroad

2015· article· en· W3124077486 on OpenAlexaboutno aff
Åke Blomqvist, Colin Busby

Bibliographic record

VenueC.D. Howe Institute Commentary · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Health careBusinessFinancePublic economicsEconomic growthEconomics
DOInot available

Abstract

fetched live from OpenAlex

Roughly 30 percent of all Canadian healthcare is privately paid for, about the same proportion as the average for the 34 industrialized countries that are members of the Organisation for Economic Cooperation and Development (OECD). However, two things make Canada’s public-private mix unique. On the one hand, there is rather limited public coverage for items such as outpatient drugs, long-term care, and dental and vision care. But on the other hand, government pays for virtually all services delivered by physicians and acute-care hospitals. With limited government budgets for healthcare, these Canadian distinctions are linked: more spending on hospitals and doctors means there is less money for other areas of healthcare. In other countries, the public-private financing mix is typically more balanced, with government plans paying for a larger share of drugs, dental and continuing care, but with more private financing for hospital and physician services. In face of widespread calls for Canadian governments to expand public coverage for services such as drugs and homecare, policymakers must confront challenging trade-offs that rest on increasing taxes to help pay for these additional benefits. In this Commentary, we argue that a major contributing factor to Canada’s unbalanced public-private healthcare mix are the unique restrictions that many provinces impose on the private financing of hospital and physician care. Many health systems in Europe and elsewhere do not have similar restrictions and devote a much larger share of public resources to drugs and long-term care while still operating equitable and high-performing healthcare systems. Relaxing provincial regulations on physicians’ private income sources, such as opt-out prohibitions, limits on fees, and private insurance bans, could build on the strengths of our current system. Expanded patient choice and competition from healthcare providers outside medicare would create incentives for politicians and bureaucrats to manage the public system more efficiently. This Commentary also examines the Canada Health Act’s restrictions on the basic principles of our universal provincial health insurance plans. It describes the more pluralistic approaches to healthcare financing and production among other countries whose systems have been ranked well above ours in both efficiency and equity dimensions. Canada’s single-payer model for hospitals and doctors may be less expensive to administer than a pluralistic one with both public and private payment. However, a single-payer system in which doctors are expected to always use the best available medical care for every patient ultimately creates an impossible dilemma, as advancing medical technology raises the cost of doing so. Our single-payer system may have led to more equal healthcare between rich and poor than would have prevailed otherwise, but it arguably has made the social policy debate focus too much on healthcare to the detriment of other programs that are at least as important in helping society’s most vulnerable.

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)0.007
metaresearch head score (Gemma)0.014
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.154
Threshold uncertainty score0.981

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0340.017
Scholarly communication0.0160.008
Open science0.0030.005
Research integrity0.0080.013
Insufficient payload (model declined to judge)0.0050.000

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 head0.301
Teacher spread0.240 · 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
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

Citations6
Published2015
Admission routes1
Has abstractyes

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