Exploring Diversity, Equity, and Inclusion–Related Pedagogy Across Different Professions
Bibliographic record
Abstract
PURPOSE: Diversity, equity, and inclusion (DEI) have become an important priority for academic medicine. However, several barriers challenge the effective implementation of DEI-related pedagogy. An exploration of the barriers to and enablers of DEI-related pedagogy-as they relate to professional contexts-can inform how to advance DEI in medical education. Shulman's notion of signature pedagogies offers a foundation for understanding and exploring the influence of such contexts on teaching and learning. Comparisons across professions may help make signature pedagogies more visible and may facilitate change. In this study, the authors aim to explore how the professional contexts of medicine, nursing, and teacher education approach DEI-related pedagogy. METHOD: The authors conducted a qualitative exploratory study using constructivist grounded theory methodology. Using both purposive and theoretical sampling, 24 participants from across the United States and Canada were interviewed, including physicians, nurses, and K-12 teachers in practice as well as professional educators in each discipline (May-December 2022). Interviews included a case-based elicitation approach, and data were analyzed iteratively across the data collection period using constant comparative analysis. RESULTS: Medicine and nursing tend to prioritize objectivity and seek to avoid or neutralize emotions that are intrinsic to DEI-related learning, view DEI expertise as being outside the purview of their profession, and view time for DEI as limited in a clinical learning environment. In contrast, teaching is built on the assumption that DEI expertise is coconstructed and inclusive of community voices and lived experiences. DEI-related pedagogy in teaching allowed for exploration of deep assumptions and learning about structural inequities. CONCLUSIONS: Findings suggest that assumptions and values held in professions, such as medicine and nursing, that valorize objectivity and neutrality, while stigmatizing vulnerability and suppressing emotions, may constrain DEI-related teaching and learning in such contexts.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".