Equity, diversity, inclusion, and anti-oppression focused faculty development: Tensions in programming in health professions education
Bibliographic record
Abstract
PURPOSE: As equity, diversity, inclusion, and anti-oppression education gains prominence in health professions education (HPE), faculty development programs are increasingly tasked with addressing systemic inequities and fostering transformative change. Little is known about the principles that guide the development of anti-oppressive faculty development programs, the decisions involved in their creation, or the challenges they face during implementation and sustainability. MATERIALS AND METHODS: This study examines five anti-oppression focused faculty development programs in North America to explore how they navigated tensions between institutional priorities, cultural change, and pedagogical practices. Using an interpretive multi-case study methodology, we analyzed program structures, participant experiences, and contextual factors to uncover common goals of these programs and tensions that they commonly experienced. We conducted the study from 2022 to 2024. RESULTS: Programs shared three overarching goals: shifting culture through faculty development, challenging oppressive structures, and encouraging open dialogue. However, each goal revealed distinct tensions. Shifting culture required balancing discomfort as a growth mechanism with the need for psychological safety and engaging resistant participants while retaining those already invested. Challenging oppressive structures exposed conflicts between institutional priorities and programmatic integrity, reliance on marginalized facilitators, and the constraints of operating within oppressive systems. Encouraging open dialogue involved addressing hierarchical norms, ensuring psychological safety, and sustaining relational learning within time and resource limitations. CONCLUSIONS: Our findings illuminate the complexities of operationalizing anti-oppressive principles in HPE. We argue that programs must ground their work in shared values, anticipate institutional pressures, and navigate competing demands with intentionality. These insights offer actionable strategies for educators and program developers seeking to foster meaningful, sustainable change in anti-oppressive faculty development.
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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 | 0.034 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.027 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.005 |
| 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".