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Record W2100854705

ASSESSING ALTERNATIVE FINANCING METHODS FOR THE CANADIAN HEALTH CARE SYSTEM IN VIEW OF POPULATION AGING

2006· article· en· W2100854705 on OpenAlexaboutno aff
Doug Andrews

Bibliographic record

VenueSocial and Economic Dimensions of an Aging Population Research Papers · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyDependency ratioHealth careGross domestic productProxy (statistics)PopulationPopulation ageingBaby boomBusinessPublic economicsEconomicsDemographic economicsActuarial scienceEconomic growthMedicineEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

The cost of the Canadian health care system is approximately 10% of Gross Domestic Product (GDP). Survey-evidence suggests that Canadians do not wish to have additional funds spent on health care but believe that the system should be able to deliver better quality care. Due to low fertility rates and increasing life expectancy, the Canadian population is aging. Over the next 25 years, the dependency ratio will increase, primarily due to the aging of the “baby boom generation” 2. This will place twofold cost pressures on governments responsible for maintaining the health care system: 1) As a consequence of increased life expectancy, on average, Canadians will have a longer period of health care consumption. Although age-specific cost may not increase, with an aging population aggregate annual health care expenditures are expected to increase. 2) The dependency ratio is a proxy for the ability of the population to support itself. The increasing dependency rate may result in a slowdown in GDP growth, given constant technology. In Section I, this paper attempts to quantify these factors. A single measure combining cost and quality is developed to demonstrate the magnitude of the challenge. In Section II, this paper examines a number of different approaches to health care financing including user fees and alternative compensation methods for physicians. The paper highlights documented information from Canada and international experience on the implementation issues involved. The paper evaluates the desirability of implementing these approaches in Canada.

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)0.000
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: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.157
GPT teacher head0.455
Teacher spread0.298 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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