Anti-oppression pedagogy in health professions: a scoping review
Bibliographic record
Abstract
Health professional learners are increasingly called to learn about health inequity to reduce inequities and improve patient care and health outcomes. Anti-oppression pedagogy (AOP) addresses the need for health professional learners to understand multiple health inequities and the structures and systems that produce inequities. However, the inclusion of AOP in health professions education varies and there is a lack of clarity in its conceptualization and integration. A scoping review was conducted to address this gap and to understand how AOP is conceptualized and integrated in health professions education. Thirty-six articles met the inclusion criteria. The articles demonstrated that AOP is not commonly utilized terminology within health professions education. When AOP is integrated, it is not consistently conceptualized but is generally viewed as a broad concept that focuses on antiracism; decoloniality; intersectionality; and supporting learners to understand, critically reflect on, and act against structural and systemic forms of oppressions. In addition, there is variation in the integration of AOP in health professions education with the most common methods consisting of discussions, cases, reflection, learning through lived experiences, and the incorporation of humanities within a longitudinal curriculum. The results of this scoping review highlight the need for health professions education to develop one clear concept that educators use when teaching about anti-oppression, which may reduce working in silos and allow educators to better collaborate with each other in advancing this work. In addition, this review suggests that health professional programs should consider incorporating AOP in curricula with a broad and longitudinal approach utilizing the common methods of delivery. To better support programs in including AOP in curricula, further research is required to emphasize the benefits, provide clarity on its conceptualization, and determine the most effective methods of integration.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".