Nonverbal behavior in prognostic communication: A pilot experiment in virtual reality
Bibliographic record
Abstract
Context: Clinicians' nonverbal behaviors contribute to patients' responses to prognosis communication, yet little empirical evidence supports precise nonverbal behaviors and the mechanisms by which they contribute to perceptions of high-quality communication. Virtual reality (VR) is a promising tool for researching communication, allowing researchers to manipulate nonverbal behaviors in controlled simulation and examine outcomes. Objectives: The goal of this pilot study was to assess whether manipulated changes in avatar doctors' nonverbal behaviors could lead to measurable differences of participant feelings, reactions, or sense of immersion in a VR scenario of prognosis communication. Methods: In this pilot experiment, university student participants were randomized to a short prognosis communication simulation in immersive VR representing one of five nonverbal conditions: No nonverbals, Smile only, Nod only, Lean only, All nonverbals. Outcomes included cognitive (e.g., cognitive load, recall), socioemotional (e.g., emotional valence, satisfaction, anxiety), and immersion and presence. Results: Our sample comprised 229 participants. Pilot experimental findings suggest that some participant responses differed as a result of the manipulation of nonverbal behaviors. However, results did not point to the presence or absence of a particular nonverbal behavior as driving reactions to prognostic communication. Conclusions: VR can allow for experimental manipulation of nonverbal behavior. There is need for further development to optimally conduct sensitive and ecologically valid communication simulations for research, and for research into discrete nonverbal behaviors to improve serious illness communication training and practice. Innovation: VR experimental simulations are a promising tool for building the evidence base of nonverbal behavior in serious illness communication.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".