Quantifying Shared Mental Models in Nursing Using Multimodal Physiological and Communication Data

Shared Mental Models (SMMs) are essential for effective nursing teamwork, yet existing assessments mainly rely on observations or self-reports that provide limited insight into team cognition during task execution. This study proposes a multimodal framework for quantifying SMM quality in virtual reality nursing simulations. Thirty nursing students (15 two-person teams) completed three collaborative clinical scenarios while electroencephalography (EEG), eye-tracking, and communication data were synchronously collected. Expert annotations were used to classify teams into high- and low-SMM groups. Results showed significant differences in neural activity, visual attention organization, and communication structures between the two groups. High-SMM teams demonstrated distinct EEG patterns, more organized gaze behaviors, and stronger connectivity between task-oriented communication components. These findings suggest that multimodal physiological and behavioral measures can provide objective indicators of SMM quality and support future development of real-time assessment tools for nursing teamwork and simulation-based education.