A critical exploration of nurses' perceptions of access to oncology care among Indigenous peoples: Results of a national survey
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".