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Record W2887753485 · doi:10.1002/hpm.2582

Provincial health expenditure and cardiovascular disease mortality, a panel data study of Canadian provinces

2018· article· en· W2887753485 on OpenAlexaffabout
Hakunawadi Alexander Pswarayi, Emmanuel Dankwah, Manpreet Kaur, Imaeyen Okon, Mohsen Yaghoubi, Tamer Qarmout, Megan Steeves, Marwa Farag

Bibliographic record

VenueThe International Journal of Health Planning and Management · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of SaskatchewanSaskatchewan Health
Fundersnot available
KeywordsHealth careMedicinePanel dataContext (archaeology)Environmental healthFixed effects modelHausman testDiseaseDemographyGerontologyGeographyEconomicsEconomic growth

Abstract

fetched live from OpenAlex

OBJECTIVE: Health expenditures on cardiovascular disease (CVD) account for a large proportion of health care expenditures of all the diseases in Canada, and hence there is a need to examine the responsiveness of CVD outcomes to health expenditures. The objective of this study was to examine the relationship between health care expenditures and CVD mortality, as a health care outcome at the provincial level in Canada. METHODS: A 10-year (2000-2009) panel dataset was constructed from multiple data sources for the purposes of this study. The dataset composed of age standardized CVD mortalities, health care expenditures, and covariates for the 10 Canadian provinces. We employed a fixed effects model based on the results of the Hausman test, with CVD mortalities as the dependent variable and health care expenditure and other covariates, as explanatory variables. RESULTS: Health care expenditures were significantly (0.05) and negatively associated with CVD mortality, with a 1% increase in health care expenditures associated with a decrease of 6.31 per 1 000 000 people in CVD mortality. CONCLUSION: In the Canadian context, increases in spending on health care were associated with improvements in CVD outcomes for the time period under investigation.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 science0.0010.001
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 head0.210
GPT teacher head0.470
Teacher spread0.260 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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