Reflections on Learning Nursing as a Black Student in Canada: A Case for Invitational Antiracist Education
Bibliographic record
Abstract
International studies have revealed that Black undergraduate nursing students experience higher levels of attrition from nursing programs. Touted as the least likely racial group to graduate, Black students struggle disproportionately in comparison to their non-Black peers. Canadian literature, however, is largely silent on this topic and this population as a whole. Grounding our reflections in one student’s experience, we argue that Canadian nurse educators need to implement invitational, antiracist approaches that intentionally support Black students’ success. This article supports our argument by reflecting on both the literature and personal experience. First, we explain our reflective processes. Then, the history of nursing in Canada is presented, followed by an exploration of the current educational landscape. Next, we discuss Black students’ experiences in nursing education. We conclude with recommendations for Canadian nurse educators.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.093 | 0.031 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.009 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 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".