Anti-racism curricula in undergraduate medical education: A scoping review
Bibliographic record
Abstract
Purpose Medical educators have increasingly focused on the systemic effects of racism on health inequities in the United States (U.S.) and globally. There is a call for educators to teach students how to actively promote an anti-racist culture in healthcare. This scoping review assesses the existing undergraduate medical education (UME) literature of anti-racism curricula, implementation, and assessment.Methods The Ovid, Embase, ERIC, Web of Science, and MedEdPORTAL databases were queried on 7 April 2023. Keywords included anti-racism, medical education, and assessment. Inclusion criteria consisted of any UME anti-racism publication. Non-English articles with no UME anti-racism curriculum were excluded. Two independent reviewers screened the abstracts, followed by full-text appraisal. Data was extracted using a predetermined framework based on Kirkpatrick’s educational outcomes model, Miller’s pyramid for assessing clinical competence, and Sotto-Santiago’s theoretical framework for anti-racism curricula. Study characteristics and anti-racism curriculum components (instructional design, assessment, outcomes) were collected and synthesized.Results In total, 1064 articles were screened. Of these, 20 met the inclusion criteria, with 90% (n = 18) published in the past five years. Learners ranged from first-year to fourth-year medical students. Study designs included pre- and post-test evaluations (n = 10; 50%), post-test evaluations only (n = 7; 35%), and qualitative assessments (n = 3; 15%). Educational interventions included lectures (n = 10, 50%), multimedia (n = 6, 30%), small-group case discussions (n = 15, 75%), large-group discussions (n = 5, 25%), and reflections (n = 5, 25%). Evaluation tools for these curricula included surveys (n = 18; 90%), focus groups (n = 4; 20%), and direct observations (n = 1; 5%).Conclusions Our scoping review highlights the growing attention to anti-racism in UME curricula. We identified a gap in published assessments of behavior change in applying knowledge and skills to anti-racist action in UME training. We also provide considerations for developing UME anti-racism curricula. These include explicitly naming and defining anti-racism as well as incorporating longitudinal learning opportunities and assessments.
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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 | 0.021 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".