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Record W4206839973 · doi:10.3390/curroncol29010018

Equity-Oriented Healthcare: What It Is and Why We Need It in Oncology

2022· article· en· W4206839973 on OpenAlexafffundvenue
Tara C. Horrill, Annette J. Browne, Kelli Stajduhar

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersMichael Smith Health Research BC
KeywordsDisadvantagedHealth equityEquity (law)Health careMedicineStigma (botany)Public relationsNursingEconomic growthPolitical sciencePsychiatryEconomicsLaw

Abstract

fetched live from OpenAlex

Alarming differences exist in cancer outcomes for people most impacted by persistent and widening health and social inequities. People who are socially disadvantaged often have higher cancer-related mortality and are diagnosed with advanced cancers more often than other people. Such outcomes are linked to the compounding effects of stigma, discrimination, and other barriers, which create persistent inequities in access to care at all points in the cancer trajectory, preventing timely diagnosis and treatment, and further widening the health equity gap. In this commentary, we discuss how growing evidence suggests that people who are considered marginalized are not well-served by the cancer care sector and how the design and structure of services can often impose profound barriers to populations considered socially disadvantaged. We highlight equity-oriented healthcare as one strategy that can begin to address inequities in health outcomes and access to care by taking action to transform organizational cultures and approaches to the design and delivery of cancer services.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.691
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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.186
GPT teacher head0.396
Teacher spread0.210 · 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 designNot applicable
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

Citations33
Published2022
Admission routes3
Has abstractyes

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