Designing Virtual Reality–Based Conversational Agents to Train Clinicians in Verbal De-escalation Skills: Exploratory Usability Study
Bibliographic record
Abstract
BACKGROUND: Violence and aggression are significant workplace challenges faced by clinicians worldwide. Traditional methods of training consist of "on-the-job learning" and role-play simulations. Although both approaches can result in improved skill levels, they are not without limitation. Interactive simulations using virtual reality (VR) can complement traditional training processes as a cost-effective, engaging, easily accessible, and flexible training tool. OBJECTIVE: In this exploratory study, we aimed to determine the feasibility of and barriers to verbal engagement with a virtual agent in the context of the Code Black VR application. Code Black VR is a new interactive VR-based verbal de-escalation trainer that we developed based on the Clinical Training Through VR Design Framework. METHODS: In total, 28 participants with varying clinical expertise from 4 local hospitals enrolled in the Western Sydney Local Health District Clinical Initiative Nurse program and Transition to Emergency Nursing Programs and participated in 1 of 5 workshops. They completed multiple playthroughs of the Code Black VR verbal de-escalation trainer application and verbally interacted with a virtual agent. We documented observations and poststudy reflection notes. After the playthroughs, the users completed the System Usability Scale and provided written comments on their experience. A thematic analysis was conducted on the results. Data were also obtained through the application itself, which also recorded the total interactions and successfully completed interactions. RESULTS: The Code Black VR verbal de-escalation training application was well received. The findings reinforced the factors in the existing design framework and identified 3 new factors-motion sickness, perceived value, and privacy-to be considered for future application development. CONCLUSIONS: Verbal interaction with a virtual agent is feasible for training staff in verbal de-escalation skills. It is an effective medium to supplement clinician training in verbal de-escalation skills. We provide broader design considerations to guide further developments in this area.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".