Embedding cultural safety in nursing education: A scoping review of strategies and approaches
Bibliographic record
Abstract
INTRODUCTION: Post-secondary institutions can no longer ignore calls to action. Nursing programs are responsible for addressing racism and discrimination by integrating cultural safety into baccalaureate nursing curricula. This scoping review of peer-reviewed literature will reveal how cultural safety is integrated into nursing education across North American post-secondary institutions. MATERIALS AND METHODS: 57 records met the criteria for this scoping review between the dates of January 2016 to July 2024. The database search was conducted from November 7, 2021, to November 9, 2021, and then updated on September 17, 2024, to include articles up to and including July 2024, yielding 3444 total search results (1448 from Medline; 1996 from CINAHL). Results were manually screened, and duplicates found were removed. RESULTS: The articles were analyzed thematically to identify strategies for integrating cultural safety into undergraduate nursing education. Four main themes were identified: experiential, theoretical, analytical, and multimodal learning. CONCLUSION: This scoping review highlights nurse educators' role in fostering cultural safety and the importance of considering multiple strategies in curricular development. Strategies include practice experiences, simulations, storytelling, and case-based learning. To provide safe nursing care, education must be inclusive and responsive to people of all races, genders, abilities, and sexual orientations. By applying the concept of cultural safety to nursing education, students are required to acknowledge bias and address issues of power, colonialism, racism, and discrimination that exist in healthcare. These findings provide a foundation for future, more focused research. Further studies could expand on this work by evaluating the effectiveness of specific pedagogical strategies and exploring ways to better support nurse educators in promoting culturally safe learning environments.
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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 | 0.049 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.051 | 0.043 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".