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Record W2884544913

The Determinants of Public Health Expenditures: Comparing Canada and Spain

2018· article· en· W2884544913 on OpenAlexaboutno aff
Livio Di Matteo, David Cantarero

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaPublic healthHealth carePer capita incomeDemographic economicsDifferential (mechanical device)Public expenditureGeographyEconomicsBusinessPublic financeEconomic growthEnvironmental healthDemographyPopulationMedicine
DOInot available

Abstract

fetched live from OpenAlex

The determinants of public health care expenditure are examined in two of the most decentralized countries in the world (Canada and Spain) for two partly overlapping time-spans of data availability: Canada, 1981 to 2013 and Spain, 2002 to 2013. While Canada generally spends more per capita on health care than Spain, over time Spain’s macro level health indicator performance has surpassed Canada’s. Using regression analysis, we find the key determinants of public health care spending include time trend, income, physician numbers and regional fixed effects. Physician numbers are a significant driver of real per capita public health expenditures in Canada but not Spain despite the greater per capita number of physicians in Spain. Differences in the growth and performance of real per capita income explain much of the gap between public health spending between these two countries with some contribution from differences in per capita physician numbers. The differential health indicator outcomes raise the question of what Canada might do to be more efficient.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.073
GPT teacher head0.340
Teacher spread0.267 · 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.

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

Citations4
Published2018
Admission routes1
Has abstractyes

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