“Let's Call a Spade a Spade. My Barrier is Being a Black Student”: Challenges for Black Undergraduate Nursing Students in a Western Canadian Province
Bibliographic record
Abstract
Background We need more understanding of experiences that hinder or promote equity, diversity, and inclusion of Black students in undergraduate nursing programs to better inform their retention and success. Purpose To explore documented experiences of Black undergraduate nursing students, review barriers affecting their retention and success, and suggest evidence-based strategies to mitigate barriers that influence their well-being. Methods We used a focused qualitative ethnography for recruiting Black former and current students (N = 18) in a Western Canadian province's undergraduate nursing programs via purposive and snowball sampling. Most participants were female, 34 years or younger, with over 50% currently in a nursing program. Five participants later attended a focus group to further validate the findings from the individual interviews. Descriptive statistics were used to describe participant characteristics; we applied a collaborative constant comparison and thematic analysis approach to their narratives. Results Challenges influencing Black students’ retention and success fell into four main interrelated subthemes: disengaging and hostile learning environments, systemic institutional and program barriers, navigation of personal struggles in disempowering learning environments, and recommendations to improve the delivery of nursing programs. Participants also recommended ways to improve diversity and mitigate these barriers, such as nursing programs offering anti-oppression courses, platforms for safe/healthy dialogue, and more culturally sensitive learning-centered programs and responsive supports. Conclusions The study findings underscore the need for research to better define nursing program conditions that nurture safe, learning-centred environments for Black students. A rethink of non-discriminatory, healthy learning–teaching engagements of Black students and the mitigation of anti-Black racism can best position institutions to promote equity, diversity, and inclusion of Black students.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.041 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".