Anti‐oppressive pedagogy in medical education: A qualitative study of trainees and faculty
Bibliographic record
Abstract
PURPOSE: Despite the proliferation of interest in health equity and justice in medical education, there is limited research into the practical implementation of pedagogical approaches that align with anti-oppressive practices. This study sought to explore how to integrate anti-oppressive pedagogy into medical education. METHODS: Using constructivist grounded theory, the authors conducted 19 semi-structured interviews with a continuum of medical education stakeholders including learners and faculty in a Canadian context between June and August 2021. Transcripts were iteratively analysed using constant comparative analysis. RESULTS: Findings suggest that existing approaches to anti-oppressive pedagogy in medical education are misaligned with the perceived values, priorities, pace, biomedical focus and hierarchical nature of medical education, and medical practice. Although some learners are motivated to advance anti-oppressive teaching, their motivations are often related to their personal experiences of oppression. Participants suggested that transformative and structural changes are required to effectively integrate anti-oppressive pedagogy into medical education. Suggestions included a shift to community-based learning while ensuring adequate compensation for educators and addressing resistance at individual and institutional levels. CONCLUSION: Anti-oppressive pedagogy does not presently align with existing medical education practices. Effectively integrating anti-oppressive approaches will require individual and institutional reflection on the values and assumptions that underpin the field before progress can be made in a meaningful and sustainable way.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".