Call to Action: Integrating Anti-racist Philosophies in Dismantling Racism and Anti-Black Racism in Nursing Education in Canada
Bibliographic record
Abstract
Despite nursing's stated mandate of health equity and social justice, concrete steps to address racism and anti-Black racism in the profession and nursing education remain mainly non-significant and are often seen as performative. It is crucial to implement tangible measures to dismantle racism and anti-Black racism in nursing education to address racial health disparities. Throughout history, nursing education has been shaped by colonial and Eurocentric ideologies, leading to the silencing and erasure of the knowledge, culture, perspectives, and ways of knowing of Black and other racialized communities. Consequently, urgent action is required to dismantle embedded racism and anti-Black racism in the nursing profession. Drawing on anti-racist philosophies, we argue that dismantling racism in nursing education goes beyond superficial discussions of equity, diversity, and inclusion. Instead, it demands a proactive approach to tackle the underlying causes of racial inequities. In this article, we propose several recommendations and implications for nursing educators, researchers, policymakers, and educational institutions to eliminate racism and anti-Black racism in both nursing education and practice. These recommendations include acknowledging the historical and contemporary impacts of racism and anti-Black racism on the health and well-being of Black individuals, engaging in critical self-reflexivity, integrating and prioritizing Black knowledge and perspectives in nursing education, practice, and research, and intentionally adopting anti-racist pedagogy.
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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 | 0.037 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.065 | 0.029 |
| Scholarly communication | 0.026 | 0.010 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.022 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".