Antidiscrimination pedagogical approaches to enhance diversity and inclusion in undergraduate nursing education: A critical analysis
Bibliographic record
Abstract
Background and objective: Nursing plays a vital role in promoting antidiscrimination pedagogical approaches within education. However, there remains a gap in developing inclusive teaching practices for ensuring culturally responsive nursing education. The objective of this study was to critically examine antidiscrimination pedagogical strategies designed to foster diversity and inclusion among undergraduate nursing students.Methods: A critical interpretive qualitative study included a convenience sample of ninety-seven participants enrolled in an undergraduate nursing program at a Canadian university. A purposive sampling and an online survey were used for data collection. An antidiscrimination pedagogical strategy was used including pre-simulation, pre-briefing, simulation, debriefing, reflection and self-evaluation. Results: Three themes emerged that focused on cultural and ethical understanding, active engagement and discussion, and gender and language illustration to understand the goals, strategies and impact of the case scenario.Conclusions: This study demonstrates that implementing antidiscriminatory pedagogical strategies in nursing education yields benefits for fostering diversity and inclusion. Fostering an inclusive, culturally responsive, and equitable safe learning environment, enhances earning outcomes and promotes professional growth.Implications: The implementation of anti-discrimination teaching pedagogy depends on nurse educators to integrate simulation-based education. Debriefing and reflection will ensure engaging students in diverse scenarios to apply responsive practices in nursing care.
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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 | 0.057 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".