Using virtual reality simulation to address racism in a healthcare setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it