Commentary: Racism and Bias in Health Professions Education: How Educators, Faculty Developers, and Researchers Can Make a Difference
Bibliographic record
Abstract
The Research in Medical Education (RIME) Program Planning Committee is committed to advancing scholarship in and promoting dialogue about the critical issues of racism and bias in health professions education (HPE). From the call for studies focused on underrepresented learners and faculty in medicine to the invited 2016 RIME plenary address by Dr. Camara Jones, the committee strongly believes that dismantling racism is critical to the future of HPE.The evidence is glaring: Dramatic racial and ethnic health disparities persist in the United States, people of color remain deeply underrepresented in medical school and academic health systems as faculty, learner experiences across the medical education continuum are fraught with bias, and current approaches to teaching perpetuate stereotypes and insufficiently challenge structural inequities. To achieve racial justice in HPE, academic medicine must commit to leveraging positions of influence and contributing from these positions. In this Commentary, the authors consider three roles (educator, faculty developer, and researcher) represented by the community of scholars and pose potential research questions as well as suggestions for advancing educational research relevant to eliminating racism and bias in HPE.
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 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 | 0.018 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.072 | 0.072 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".