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Record W2159834688 · doi:10.1377/hlthaff.2009.0669

Lesson From Canada’s Universal Care: Socially Disadvantaged Patients Use More Health Services, Still Have Poorer Health

2011· article· en· W2159834688 on OpenAlexafffundabout
David A. Alter, Thérèse A. Stukel, Alice Chong, David Henry

Bibliographic record

VenueHealth Affairs · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsInstitute for Clinical Evaluative Sciences
FundersCanadian Institutes of Health Research
KeywordsDisadvantagedSocioeconomic statusHealth careHealth equityMedicineEnvironmental healthUniversal health careHealth policyGerontologyPublic healthEconomic growthNursingEconomicsPopulation

Abstract

fetched live from OpenAlex

Lower socioeconomic status is commonly related to worse health. If poor access to health care were the only explanation, universal access to care should eliminate the association. We studied 14,800 patients with access to Canada's universal health care system who were initially free of cardiac disease, tracking them for at least ten years and seven months. We found that socially disadvantaged patients used health care services more than did their counterparts with higher incomes and education. We also found that service use by people with lower incomes and less education had little impact on their poorer health outcomes, particularly mortality. Countries contemplating national health insurance cannot rely on universal health care to eliminate historical disparities in outcomes suffered by disadvantaged groups. Universal access can only reduce these disparities. Our findings suggest the need to introduce large-scale preventive strategies early in patients' lives to help change unhealthy behavior.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.303
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.305
Teacher spread0.276 · 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

Citations61
Published2011
Admission routes3
Has abstractyes

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