“You have to strive very hard to prove yourself”: experiences of Black nursing students in a Western Canadian province
Bibliographic record
Abstract
OBJECTIVES: This study explored the experiences of Black students in two western Canadian undergraduate nursing programs. METHODS: Using a qualitative focused ethnography design grounded in critical race theory and intersectionality, participants were recruited using purposive and snowball sampling. Data were collected through individual interviews, and a follow-up focus group. Data were analyzed using collaborative-thematic analysis team approaches. RESULTS: n=18 current and former students participated. Five themes emerged: systemic racism in nursing, precarious immigrant context, mental health/well-being concerns, coping mechanisms, and suggestions for improvement. CONCLUSIONS: An improved understanding of Black student experiences can inform their recruitment and retention. Supporting Black students' success can potentially improve equity, diversity, and inclusivity in nursing education programs and/or their representation in the Canadian nursing workforce. IMPLICATIONS FOR AN INTERNATIONAL AUDIENCE: The presence of a diverse nursing profession is imperative to meet the needs to provide more quality and culturally competent services to diverse population.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.044 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".