Racism and antiracism in nursing education: confronting the problem of whiteness
Bibliographic record
Abstract
BACKGROUND: Systemic racism in Canadian healthcare may be observed through racially inequitable outcomes, particularly for Indigenous people. Nursing approaches intending to respond to racism often focus on culture without critically addressing the roots of racist inequity directly. In contrast, the critical race theory approach used in this study identifies whiteness as the underlying problem; a system of racial hierarchy that accords value to white people while it devalues everyone else. METHODS: This qualitative study seeks to add depth to the understanding of how whiteness gets performed by nursing faculty and poses antiracism education as a necessary tool in addressing the systemic racism within Canadian healthcare. The methodology of poststructural discourse analysis is used to explore the research question: how do white nursing faculty draw on common discourses to produce themselves following introductory antiracism education? RESULTS: Analysis of data reveals common patterns of innocent and superior white identity constructions including benevolence, neutrality, Knowing, and exceptionalism. While these patterns are established in other academic fields, the approaches and results of this study are not yet common in nursing literature. CONCLUSIONS: The findings highlight the need for antiracism education at personal and policy levels beginning in nursing programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".