Development of a virtual simulation to support bystanders in responding to racism in the classroom in health professions education
Bibliographic record
Abstract
• Virtual simulations offer an engaging method to support students to address racism. • Inclusive design with students, faculty, and DEI experts ensures authenticity. • Open-access antiracism modules aim to foster inclusive learning spaces. Racism in health professions education undermines academic success and well-being of students who are Black, Indigenous, and People of Color. Bystanders often lack strategies to intervene effectively without causing further harm. We cocreated a virtual simulation module with diverse faculty and student input to equip bystanders with antiracism strategies, guided by the ARISE Bystander Model. We developed two virtual simulations: (a) identifying covert and overt racism, and (b) practicing bystander interventions in the classroom. The process integrated lived experiences, expert review, and sensitivity to equity-deserving perspectives. The module also includes preparatory materials, prebriefing, and debriefing resources to support reflection. This open-access module addresses a critical gap in education by providing an innovative, accessible resource for teaching health professionals how to navigate and intervene in racist incidents. Virtual simulation offers an interactive and immersive way to engage students, promoting empathy, education, and allyship. Its wide reach demonstrates potential for simulation-based learning to create inclusive environments. Challenges included coordination and limited Indigenous representation, highlighting areas for improvement in future projects.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".