A Study on the Mechanism Affecting the Innovation and Entrepreneurship Ability of Medical Students Based on Constructivist Theory: Mediating Role of Innovation and Entrepreneurship Willingness

Hongxu Pan¹Yujia Jiang¹Yu Huang¹Zhihao Zhang²Junjie Du³Xuejin Song⁴Jianjun Zhu⁵Yanlin Xie⁶Chenchen Xu^7*Yong Li^3*

• ¹School of Stomatology, Nanjing Medical University, Nanjing 211166, Jiangsu, China, Nanjing, China
• ²Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, Beijing, China, Beijing, China
• ³Department of Cardiology,the First Affiliated Hospital with Nanjing Medical University，Nanjing 212028，Jiangsu, China, Nanjing, China
• ⁴School of Information Science and Engineering, Southeast University, Nanjing 211102, Jiangsu，China, Nanjing, China
• ⁵Second Clinical Medical College, Nanjing Medical University, Nanjing 211166, Jiangsu, China, Nanjing, China
• ⁶First Clinical Medical College, Nanjing Medical University, Nanjing 211166, Jiangsu, China, Nanjing, China
• ⁷Fourth Clinical Medical College, Nanjing Medical University, Nanjing 211166, Jiangsu, China, Nanjing, China

Background: Mass innovation and entrepreneurship (IE) have become a global trend, yet medical education systems often fail to meet students' innovation development needs. This study examines how universities can enhance medical students' IE Willingness and ability. Methods: By analyzing survey data from 1,467 medical students under Constructivist Theory, we employed Amos 23.0 and SPSS 29.1 for cross-sectional path analysis. The study measured latent constructs—IE recognition, training courses, research talent models, incentive mechanisms, willingness, and ability—through 36 observed variables. Results: The analysis revealed highly significant direct effects on IE Willingness from the Research Talent Training Models (β=0.857), IE Incentive Mechanism (β=0.731), innovation courses (β=0.696), and IE recognition (β=0.521). For mediation effects on IE Ability (via willingness), only IE recognition (β=0.122) and incentive mechanisms (β=0.064) showed significance, while research models (β=0.053) and innovation courses (β=0.062) were non-significant. These findings suggest that optimizing incentive mechanisms, refining innovation courses, and strengthening research talent models—the most impactful driver—can effectively foster medical students' innovation engagement.