An Introduction to Faculty Diversity, Equity, and Inclusion for Excellence in Nurse Education: Literature Review
Bibliographic record
Abstract
BACKGROUND: The diversity of the world's population is increasing, along with the health inequities of underrepresented minority populations. To provide high-quality care to all patients, nurses require an understanding of diversity, equity, and inclusion (DEI) as well as how to implement best practices. Nurse educators are the ones to lead the way for DEI education for students. OBJECTIVE: This paper aims to describe the findings of a literature review that introduces DEI concepts for excellence in nurse education and their related benefits. Best practices for actions to address DEI in nursing education will be described. METHODS: After institutional review board approval, a literature search yielded 61 articles using 15 distinct keywords in 4 global, peer-reviewed literature databases. Melynk and Fineout-Overholt's (2023) Levels of Evidence guided the process of selecting 26 peer-reviewed articles and resources. RESULTS: Common themes for best practices in DEI were identified. These themes included recruiting underrepresented minority nursing faculty, incorporating DEI into an institution's mission statement, addressing DEI topics in curricula, providing leadership, having a DEI strategic plan, developing education, developing data-based interventions, instilling policy change, partnering in outreach, targeting impact on hiring committees, recognizing DEI work, and providing mentorship. CONCLUSIONS: In summary, this literature review provides several strategies to address DEI for nurse educators. Committing to DEI efforts and improving diversity in the nurse educator workforce are integral steps in improving the quality and inclusivity of nursing education and ultimately improving the health of our communities.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.032 | 0.028 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".