Using virtual reality simulation to address racism in a healthcare setting
Bibliographic record
Abstract
Racism continues to plague Western societies' institutions, including the healthcare system. Despite the evidence of racism's negative impacts on healthcare providers, administrators, patients, and families, healthcare workers report hesitancy in taking action to address racism in the workplace. Simulation, with its experiential pedagogy and foundation in psychological safety, may be an educational tool to support practical training. Guided by a social cognitive view of regulation of learning, we piloted virtual reality (VR) modules focused on addressing bias, privilege, and microaggressions. We used pre-/post-surveys, reflective journals, built-in VR platform data, and simulation debriefing session notes to better understand the effectiveness and usability of these VR modules in our organization. Overall, participants found the VR modules highly valuable, and we noted a shift in participants' reported intentions to take action to address racism in the workplace. Participants also noted the importance of a multifaceted plan that goes beyond education to ensure a meaningful culture shift toward addressing racism at work. Practical lessons from this pilot study included the necessity of an informed debriefing plan focused on participants' positionality and power and the need to deeply understand our institution's information technology (IT) environment to ensure successful deployment of VR technology.
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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 | 0.005 | 0.013 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".