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Record W3187865383 · doi:10.1111/nin.12446

A critical exploration of nurses' perceptions of access to oncology care among Indigenous peoples: Results of a national survey

2021· article· en· W3187865383 on OpenAlexaffabout
Tara C. Horrill, Donna Martin, Josée G. Lavoie, Annette Schultz

Bibliographic record

VenueNursing Inquiry · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsManitoba HealthUniversity of ManitobaBC Cancer Agency
Fundersnot available
KeywordsIndigenousHealth careOncologyMedicineNursingNarrativeInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Inequities in access to oncology care among Indigenous peoples in Canada are well documented. Access to oncology care is mediated by a range of factors; however, emerging evidence suggests that healthcare providers, including nurses, play a significant role in shaping healthcare access. The purpose of this study was to critically examine access to oncology care among Indigenous peoples in Canada from the perspective of oncology nurses. Guided by postcolonial theoretical perspectives, interpretive descriptive and critical discourse analysis methodologies informed study design and data analysis. Oncology nurses were recruited from across Canada to complete an online survey (n = 78). Nurses identified a range of barriers experienced by Indigenous peoples when accessing oncology care, yet located these barriers primarily at the individual and systems levels. Nurses perceived themselves as mediators of access to oncology care; however, their efforts to facilitate access to care were constrained by the dominance of biomedicine within healthcare. Nurses' constructions of access to oncology care highlight the embedded narrative of individualism within nursing practice and the relative invisibility of racism as a determinant of equitable access to care among Indigenous peoples. This suggests a need for oncology nurses to better understand and incorporate structural determinants of health perspectives.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.242
GPT teacher head0.556
Teacher spread0.314 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2021
Admission routes2
Has abstractyes

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