Exploring the concepts of diversity, equity, and inclusion in nursing curricula through an integrative review
Bibliographic record
Abstract
Objective: The aim of this review was to explore the concepts of diversity, equity, inclusion within nursing education. Moving forward with the new AACN Essentials, nursing professors will be required to integrate these concepts into the curriculum, therefore it is important to understand what is currently available in the literature.Methods: An exhaustive review of the literature revealed that among the original 3,107 articles found, only 24 were included because of the narrowed IR focus on curriculum. Articles were then evaluated for suggested learning activities.Results: This IR reviews 24 articles in total. Some of the articles reviewed contained more than one concept. Of the articles reviewed, 18 focused on inclusion, 8 on equity, and 13 on diversity.Conclusions: The findings first and foremost indicate the need to incorporate teaching and learning practices related to the concepts of diversity, equity, and inclusion and secondly the need to publish additional best practices and exemplars.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".