Advancing anti-oppression and social justice in healthcare through competency-based medical education (CBME)
Bibliographic record
Abstract
Competency-based medical education (CBME) focuses on preparing physicians to improve the health of patients and populations. In the context of ongoing health disparities worldwide, medical educators must implement CBME in ways that advance social justice and anti-oppression. In this article, authors describe how CBME can be implemented to promote equity pedagogy, an approach to education in which curricular design, teaching, assessment strategies, and learning environments support learners from diverse groups to be successful. The five core components of CBME programs - outcomes competency framework, progressive sequencing of competencies, learning experiences tailored to learners' needs, teaching focused on competencies, and programmatic assessment - enable individualization of learning experiences and teaching and encourage learners to partner with their teachers in driving their learning. These educational approaches appreciate each learner's background, experiences, and strengths. Using an exemplar case study, the authors illustrate how CBME can afford opportunities to enhance anti-oppression and social justice in medical education and promote each learner's success in meeting the expected outcomes of training. The authors provide recommendations for individuals and institutions implementing CBME to enact equity pedagogy.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".