Contending with Our Racial Past in Medical Education: A Foucauldian Perspective
Bibliographic record
Abstract
Issue Practices of systemic and structural racism that advantage some groups over others are embedded in American society. Institutions of higher learning are increasingly being pressured to develop strategies that effectively address these inequities. This article examines medical education’s diversity reforms and inclusion practices, arguing that many reify preexisting social hierarchies that privilege white individuals over those who are minoritized because of their race/ethnicity. Evidence: Drawing on the work of French theorist Michel Foucault, we argue that medical education’s curricular and institutional practices reinforce asymmetrical power differences and authority in ways that disadvantage minoritized individuals. Practices, such as medical education’s reliance on biomedical approaches, cultural competency, and standardized testing reinforce a racist system in ways congruent with the Foucauldian concept of “normalization.” Through medical education’s creation of subjects and its ability to normalize dominant forms of knowledge, trainees are shaped and socialized into ways of thinking, being, and acting that continue to support racial violence against minoritized groups. The systems, structures, and practices of medical education need to change to combat the pervasive forces that continue to shape racist institutional patterns. Individual medical educators will also need to employ critical approaches to their work and develop strategies that counteract institutional systems of racial violence. Implications: A Foucauldian approach that exposes the structural racism inherent in medical education enables both thoughtful criticism of status-quo diversity practices and practical, theory-driven solutions to address racial inequities. Using Foucault’s work to interrogate questions of power, knowledge, and subjectivity can expand the horizon of racial justice reforms in medicine by attending to the specific, pervasive ways racial violence is performed, both intra- and extra-institutionally. Such an intervention promises to take seriously the importance of anti-racist methodology in medicine.
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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 | 0.033 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.020 | 0.181 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".