Towards anti-racist futures: a scoping review exploring educational interventions that address systemic racism in post graduate medical education
Bibliographic record
Abstract
Since 2020, brought to the forefront by movements such as Black Lives Matter and Idle No More, it has been widely acknowledged that systemic racism contributes to racially differentiated health outcomes. Health professional educators have been called to address such disparities within healthcare, policy, and practice. To tackle structural racism within healthcare, one avenue that has emerged is the creation of medical education interventions within postgraduate residency medical programming. The objective of this scoping review is to examine the current literature on anti-racist educational interventions, that integrate a systemic or structural view of racism, within postgraduate medical education. Through the identification and analysis of 23 papers, this review identified three major components of interest across medical interventions, including (a) conceptualization, (b) pedagogical issues, and (c) outcomes & evaluation. There were overlapping points of discussion and analysis within each of these components. Conceptualization addressed how researchers conceptualized racism in different ways, the range of curricular content educators chose to challenge racism, and the absence of community's role in curricular development. Pedagogical issues addressed knowledge vs. skills-based teaching, and tensions between one-time workshops and integrative curriculum. Outcomes and evaluation highlighted self-reported Likert scales as dominant types of evaluation, self-evaluation in educational interventions, and misalignments between intervention outcomes and learning objectives. The findings are unique in their in-depth exploration of anti-racist medical interventions within postgraduate medical education programming, specifically in relation to efforts to address systemic and structural racism. The findings contribute a meaningful review of the current state of the field of medical education and generate new conversations about future possibilities for a broader anti-racist health professions curriculum.
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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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".