“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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".