MétaCan
Menu
Back to cohort
Record W2513027210 · doi:10.7939/r3-0t6f-mf21

Healthcare in Canada's North: Are We Getting Value for Money?

2016· article· en· W2513027210 on OpenAlexaffabout
T. Kue Young, Susan Chatwood, Gregory P. Marchildon

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsPublic Health OntarioInstitute for Circumpolar Health ResearchUniversity of Alberta
Fundersnot available
KeywordsHealth carePer capitaGovernment (linguistics)Value (mathematics)Rest (music)GeographyDemographic economicsEconomic growthSocioeconomicsBusinessEconomicsEnvironmental healthMedicinePopulation

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine if Canadians are getting value for money in providing health services to our northern residents. METHOD: Secondary analyses of data from Statistics Canada, the Canadian Institute of Health Information and territorial government agencies on health status, health expenditures and health system performance indicators. RESULTS: Per capita health expenditures in Canada's northern territories are double that of Canada as a whole and are among the highest in the world. The North lags behind the rest of the country in preventable mortality, hospitalization for ambulatory care sensitive conditions and other performance indicators. DISCUSSION: The higher health expenditure in the North is to be expected from its unique geography and demography. If the North is not performing as well as Canada, it is not due to lack of money, and policy makers should be concerned about whether healthcare can be as good as it could be.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.950
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0100.004
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.070
GPT teacher head0.322
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2016
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicIndigenous Studies and EcologyFrench-language works237,207