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Record W2971549209 · doi:10.15353/rea.v12i4.1898

Spend Less, Get More? Explaining Health Spending and Outcome Differences Between Canada and Italy

2020· preprint· en· W2971549209 on OpenAlexaffvenueabout
Livio Di Matteo, Thomas Barbiero

Bibliographic record

VenueReview of Economic Analysis · 2020
Typepreprint
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsToronto Metropolitan UniversityLakehead University
Fundersnot available
KeywordsLife expectancyPer capitaHealth spendingDemographic economicsSocial determinants of healthHealth careInfant mortalityEconomicsDemographyEconomic growthSociologyHealth insurancePopulationDeveloping country

Abstract

fetched live from OpenAlex

Canada spends more than Italy on health per capita and as a share of GDP and has a higher per capita GDP. Yet, life expectancy and infant mortality in Italy are better and have improved more over time. The implication is that the Italian health care system provides better value for money. We examine whether Italy does get better health outcomes at lower costs. Using regression analysis, we find that health spending is determined by similar drivers in both Canada and Italy. We also find that more social spending and health spending in either country do not satisfactorily explain the differences in health outcomes, suggesting the importance of broader socio-economic determinants like income and life-style choices. We conclude that while the levels of per capita health spending in Canada are higher than Italy, this partly reflects historical inertia in Canadian health spending partially attributable to the higher costs of health professionals relative to Italy.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.227
GPT teacher head0.483
Teacher spread0.256 · 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

Citations1
Published2020
Admission routes3
Has abstractyes

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