Six ways to get a grip by calling-out racism and enacting allyship in medical education
Bibliographic record
Abstract
Actively addressing racism in our faculties of medicine is needed now, more than ever. One way to do this is through allyship, the practice of unlearning and re-evaluating, in which a person in a position of privilege and power seeks to operate in solidarity with a traditionally marginalized group. In this paper, we provide practical tips on how to practice allyship, giving educators and leaders background understanding and important tools on how to actively promote equity and diversity. We also share tips on how to promote inclusivity to more accurately reflect the communities we serve. Through six broad actions of being, knowing, feeling, doing, promoting, and acting, we can empower individuals to become allies and address racism in medical education and beyond. Creating psychologically safe spaces, educating ourselves on our complex histories and how they influence the present, recognizing racism, and advocating for change, augments awareness from which we can pivot conversations. Acknowledging potential feelings of shame, guilt, and embracing our loss of privilege, allow necessary, but challenging, personal growth to occur. Finally, dismantling the racist structures that exist within medicine, moving us beyond individual interventions, will address the systemic nature of racism in medicine. Everyone can find a starting place within this guide, as simple, consistent actions foster change in our spheres of influence; and the ripple effect of these changes will impact attitudes and behaviours broadly.
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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 | 0.048 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.042 | 0.063 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.011 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".