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An emergent challenge in geriatric care is improving the quality of care, which requires insight from stakeholders. Qualitative methods offer detailed insights, but they can be biased and have limited generalizability, while quantitative methods may miss nuances. To address these limitations, network-based approaches such as Epistemic Network Analysis (ENA) can bridge the methodological gap. By leveraging the strengths of both methods, ENA provides profound insights into healthcare expert interviews. In this paper, to better understand geriatric care attitudes, we interviewed ten nursing assistants, used ENA to analyze the data, and compared their real-life daily activities with training experiences. A two-sample t-test with a large effect size (Cohen’s d = 1.63) indicated a significant difference between real-life and training activities. The findings suggested incorporating more empathetic training scenarios into the future design of our geriatric care simulation. The results have implications for human-computer interaction and effective nursing training. This is illustrated by presenting an example of using quantitative ethnography to analyze expert interviews with nursing assistants as caregivers and inform subsequent simulation and design processes. 

An emergent challenge in geriatric care is improving the quality of care, which requires insight from stakeholders. Qualitative methods offer detailed insights, but they can be biased and have limited generalizability, while quantitative methods may miss nuances. To address these limitations, network-based approaches such as Epistemic Network Analysis (ENA) can bridge the methodological gap. By leveraging the strengths of both methods, ENA provides profound insights into healthcare expert interviews. In this paper, to better understand geriatric care attitudes, we interviewed ten nursing assistants, used ENA to analyze the data, and compared their real-life daily activities with training experiences. A two-sample t-test with a large effect size (Cohen’s d = 1.63) indicated a significant difference between real-life and training activities. The findings suggested incorporating more empathetic training scenarios into the future design of our geriatric care simulation. The results have implications for human-computer interaction and effective nursing training. This is illustrated by presenting an example of using quantitative ethnography to analyze expert interviews with nursing assistants as caregivers and inform subsequent simulation and design processes. 

We present an Augmented Reality (AR) experience, enabling user interaction with a Virtual Human (VH) of an older adult. We demonstrate the feasibility of the technology to foster communication and social connection between caregivers (users) and older adults (the VH). We developed a 3D model of an embodied virtual geriatric patient that demonstrates awareness of its environment and conversations, and implemented a networking protocol for remote response control with a human in the loop.