MétaCan
Menu
Back to cohort
Record W4230060134 · doi:10.32920/ryerson.14646672

An analysis of the relationship between healthcare spending and health outcomes: a data analytics perspective using the theory of production functions

2021· preprint· en· W4230060134 on OpenAlexaffabout
Kwadwo Oppong Adu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHealth careLife expectancyPer capitaAnalyticsPanel dataPopulationActuarial scienceBusinessEconomicsMedicineEconometricsEconomic growthEnvironmental healthData scienceComputer science

Abstract

fetched live from OpenAlex

This research investigates the relationship between per capita spending on healthcare and population health outcomes at the provincial level in Canada using data from 1980 to 2010. The health outcomes examined include life expectancy at birth and at age 65, number of infant deaths, and potential years of life lost from treatable causes, all of which are separated by gender. Using analytics methods as an application of the theory of growth accounting, the study evaluates the performance of the provincial health care systems in terms of their ability to efficiently produce longevity. The study also specifies the categories of healthcare spending which are most influential in determining the efficient production of longevity and measures the contribution of healthcare spending to the determination of infant mortality and deaths from treatable causes. The methods employed include Data Envelopment Analysis, Decision Tree Induction, and Multivariate Adaptive Regression Splines. The results of the analysis point to the fact that Canada’s provinces operate inefficiently in their production of health outcomes and confirm the importance of healthcare spending to determining health outcomes 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)0.008
metaresearch head score (Gemma)0.003
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 score0.182
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.002
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 head0.562
GPT teacher head0.577
Teacher spread0.015 · 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
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicGlobal Health Care IssuesFrench-language works237,207