“Who has been here that looks like me?”: A narrative inquiry into Black, Indigenous, and People of Color graduate nursing students' experiences of white academic spaces
Bibliographic record
Abstract
Canadian Schools of Nursing rest upon white, colonial legacies that have shaped and defined what is valued as nursing knowledge and pedagogy. The diversity that exists in clinical nursing and is emerging within the graduate student population is not currently reflected within nursing faculty and academic leadership. Black, Indigenous, and People of Color (BIPOC) nurse leaders, historically and presently, are repeatedly left unacknowledged as knowers and keepers of nursing knowledge. This lack of diversity persists across nursing knowledge generation, research, and healthcare practices that ultimately aim to serve the increasingly diverse Canadian population. This narrative inquiry study examined the experiences of eight BIPOC graduate nursing students as they navigated white academic nursing spaces. The findings are presented to reflect their experiences of entrenched in whiteness, erasure of identity, and navigating belonging. These study findings highlight the importance of surfacing academic nursing history shaped by colonialism and racism, the need to diversify nursing faculty and the graduate nursing student population, and implementing nursing curricular and syllabi audits to ensure that they reflect the multitude of ways of knowing to expand dominant Eurocentric and Western knowledge in nursing education.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.033 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".