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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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; both teacher heads agree on what is shown here.
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".