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Record W2117792621 · doi:10.1017/s147895151200106x

Disparities in cancer care: Perspectives from the front line

2013· article· en· W2117792621 on OpenAlexaff
Patricia A. Miller, Christina Sinding, Patti McGillicuddy, Judy Gould, Donna Fitzpatrick‐Lewis, Linda Learn, Jennifer Wiernikowski, Margaret I. Fitch

Bibliographic record

VenuePalliative & Supportive Care · 2013
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreJuravinski Cancer CentreHamilton Health SciencesUniversity of TorontoUniversity Health NetworkPublic Health OntarioMcMaster University
Fundersnot available
KeywordsHealth careThematic analysisQualitative researchFront lineWitnessEquity (law)NursingPsychologyHealth equityMedicineSociologyPublic healthPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this qualitative study was to investigate how frontline healthcare professionals witness and understand disparity in cancer care. METHOD: Six healthcare providers from a range of care settings, none with < 15 years of frontline experience, engaged with researchers in an iterative process of identifying and reflecting on equity and disparity in cancer care. This knowledge exchange began with formal interviews. Thematic analysis of the interviews form the basis of this article. RESULTS: Participants drew attention to health systems issues, the meaning and experience of discontinuities in care for patients at personal and community levels, and the significance of social supports. Other concerns raised by participants were typical of the literature on healthcare disparities. SIGNIFICANCE OF RESULTS: Providers at the front lines of care offer a rich source of insight into the operation of disparities, pointing to mechanisms rarely identified in traditional quantitative studies. They are also well positioned to advocate for more equitable care at the local level.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.067
GPT teacher head0.364
Teacher spread0.297 · 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

Citations12
Published2013
Admission routes1
Has abstractyes

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