Barriers and Challenges Encountered in Nursing Programs by Black Undergraduate Nursing Students in Canada and the United States of America: A Scoping Review Protocol
Bibliographic record
Abstract
Purpose: The primary aim of this scoping review is to explore and map the barriers and challenges faced by Black undergraduate nursing students in Canada and the United States. Given the scarcity of existing research on this topic, the review seeks to identify gaps in the literature that can inform the development of culturally responsive interventions and strategies tailored to support these students in their nursing programs. Expected Outcomes: Identification of Barriers: The review will highlight specific academic and non-academic challenges that hinder the success of Black nursing students, such as systemic racism, lack of representation among faculty, and inadequate institutional support. Mapping Existing Research: By cataloging the available studies from 2004 to 2024, the review will offer an in-depth assessment of the current state of research regarding Black nursing students in both countries. Culturally Responsive Strategies: The findings will guide the development of targeted interventions aimed at enhancing the educational experience and success rates of Black nursing students, ultimately contributing to a more equitable nursing education landscape. Policy Recommendations: Insights from the review will inform nursing programs and policymakers about necessary changes to support Black students, addressing systemic inequities within the education system. Future Research: This scoping review will lay the groundwork for further studies aimed at understanding and mitigating the unique challenges faced by Black nursing students, fostering an environment that promotes diversity and inclusion 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 | 0.095 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".