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Record W2167562932 · doi:10.1093/hsw/hlt022

Better Colon Cancer Care for Extremely Poor Canadian Women Compared with American Women

2013· article· en· W2167562932 on OpenAlexafffundabout
Kevin M. Gorey, Isaac Luginaah, Emma Bartfay, Guangyong Zou, Sundus Haji-Jama, Eric J. Holowaty, Caroline Hamm, Sindu Kanjeekal, Frances C. Wright, Madhan K Balagurusamy, Nancy L. Richter

Bibliographic record

VenueHealth & Social Work · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsAssociation of Universities and Colleges of CanadaSunnybrook Health Science CentrePublic Health OntarioOntario Tech UniversityWindsor Regional HospitalWestern UniversityUniversity of Windsor
FundersNational Center for Chronic Disease Prevention and Health PromotionNational Cancer InstituteRobarts Research InstituteCenters for Disease Control and PreventionCalifornia Department of Public HealthUniversity of WindsorCancer Care OntarioUniversity of TorontoCanadian Institutes of Health ResearchUniversity of Southern California
KeywordsHealth careMedicineCancerColorectal cancerDemographyBreast cancerHealth insuranceGerontologyPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

Extremely poor Canadian women were recently observed to be largely advantaged on most aspects of breast cancer care as compared with similarly poor, but much less adequately insured, women in the United States. This historical study systematically replicated the protective effects of single- versus multipayer health care by comparing colon cancer care among cohorts of extremely poor women in California and Ontario between 1996 and 2011. The Canadian women were again observed to have been largely advantaged. They were more likely to have received indicated surgery and chemotherapy, and their wait times for care were significantly shorter. Consequently, the Canadian women were much more likely to experience longer survival times. Regression analyses indicated that health insurance nearly completely explained the Canadian advantages. Implications for contemporary and future reforms of U.S. health care are discussed.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.016
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.258
Teacher spread0.230 · 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

Citations19
Published2013
Admission routes3
Has abstractyes

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