Development and Implementation of a Stress Monitoring Paradigm Using Virtual Reality Simulation During the COVID-19 Pandemic
Bibliographic record
Abstract
Healthcare providers, particularly during the COVID-19 crisis, have been forced to make difficult decisions and have reported acting in ways that are contrary to their moral values, integrity, and professional commitments, given the constraints in their work environments. Those actions and decisions may lead to healthcare providers' moral suffering and distress. This work outlines the development of the Moral Distress Virtual Reality Simulator (Moral Distress VRS) to research stress and moral distress among healthcare workers during the COVID-19 pandemic. The Moral Distress VRS was developed based on the agile methodology framework, with three simultaneous development streams. It followed a two-week sprint cycle, ending with meetings with stakeholders and subject matter experts, whereby the project requirements, scope, and features were revised, and feedback was provided on the prototypes until reaching the final prototype that was deployed for in-person study sessions. The final prototype had two user interfaces (UIs), one for the participant and one for the researcher, with voice narration and customizable character models wearing medical personal protective equipment, and followed a tree-based dialogue scenario, outputting a video recording of the session. The virtual environment replicated an ICU nursing station and a fully equipped patient room. We present the development process that guided this project, how different teams worked together and in parallel, and detail the decisions and outcomes in creating each major component within a limited deadline. Finally, we list the most significant challenges and difficulties faced and recommendations on how to solve them.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".