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Record W2754072751 · doi:10.12927/hcpol.2017.25192

Is Canadian Healthcare Affordable? A Comparative Analysis of the Canadian Healthcare System from 2004 to 2014

2017· article· en· W2754072751 on OpenAlexfundvenueaboutno aff
Lesley Soril, Ted D. Adams, Madeleine Phipps-Taylor, Ul­rika Winblad, Fiona Clement

Bibliographic record

VenueHealthcare policy · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsHealth careHealthcare systemBusinessEconomic growthEconomics

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare cost-related non-adherence (CRNA), serious problems paying medical bills and average annual out-of-pocket cost over time in five countries. METHODS: Repeated cross-sectional analysis of the Commonwealth Fund International Health Policy survey from 2004 to 2014. Responses were compared between Canada, the UK, Australia, New Zealand and the US. RESULTS: Compared to the UK, respondents in Canada, Australia and New Zealand were two to three times and respondents in the US were eight times more likely to experience CRNA; these odds remained stable over time. From 2004 to 2014, Canadian respondents paid US $852-1,767 out-of-pocket for care. The US reported the largest risks of serious problems paying for care (13-18.5%), highest out-of-pocket costs (US $2,060-3,319) and greatest rise in expenditures. INTERPRETATION: Over the 10-year period, financial barriers to care were identified in Canada and internationally. Such persistent challenges are of great concern to countries striving for equitable access to healthcare.

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)0.012
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 score0.881
Threshold uncertainty score0.867

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.024
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.089
GPT teacher head0.335
Teacher spread0.246 · 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

Citations16
Published2017
Admission routes3
Has abstractyes

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