Data-Driven Development of an Elderly Training Package Using the GCC Model

Abstract

This study aimed to design and assess the effectiveness of an elderly training package for first-year students at Yibin University, China, based on the GCC Model for geriatric rehabilitation. The goal was to integrate theoretical knowledge with practical skills in geriatric care, using data-driven approaches to evaluate its impact on student learning outcomes. A purposive sample of 17 experts and 30 first-year students enrolled in geriatric rehabilitation courses participated in the study. Data were collected through a combination of in-person surveys, telephone interviews, and email interviews using the Delphi Method. The training package focused on critical aspects of geriatric care, including aging-related health issues, physical rehabilitation, psychological support, and social integration. Additionally, it incorporated technology, practical simulations, case studies, and feedback mechanisms to enhance healthcare professionals’ skills. Data analysis demonstrated a significant improvement in students' knowledge and practical abilities post-intervention, with moderate satisfaction expressed by both experts and students regarding the effectiveness of the package. The study underscores the importance of blending theoretical learning with hands-on experience, utilizing data-driven evaluation methods to assess the impact on educational outcomes. These findings provide valuable insights for the development of effective geriatric care training models that combine data science and educational practices to optimize learning in healthcare education.