Drivers of iPad use by undergraduate medical students: the Technology Acceptance Model perspective

Abstract

Abstract Background Medical students need to acquire a continuously growing body of knowledge during their training and throughout their practice. Medical training programs should aim to provide students with the skills to manage this knowledge. Mobile technology, for example, could be a strategy used through training and practice. The objective of this study was to identify drivers of using mobile technology (an iPad) in a UGME preclinical settings and to study the evolution of those drivers over time. Methods We solicited all students from two cohorts of a preclinical component of a Canadian UGME program. They were asked to answer two online surveys: one on their first year of study and another on the second year. Surveys were built based on the Technology Acceptance Model (TAM) to which other factors were also added. Data from the two cohorts were combined and analysed with partial least squares structural equation modelling (PLS-SEM) to test two measurement models, one for each year. Results We tested fifteen hypotheses on both data sets (first year and second year). Factors that explained the use of an iPad the first year were knowledge, preferences, perceived usefulness and anticipation. In the second year, perceived usefulness, knowledge and satisfaction explained the use of an iPad. Other factors have also significantly, but indirectly influenced the use of the iPad. Conclusions We identified factors that influenced the use of an iPad in a preclinical medical program. These factors differed from the first year to the second year in the program. Our results suggest that interventions should be tailored for different point in time to foster the use of an iPad. Further study should investigate how interventions based on these factors may influence implementation of mobile technology to help students acquire ability to navigate efficiently through medical knowledge.

Keywords

• Mobile technology
• Technology Acceptance Model
• Predictive modeling
• Undergraduate medical education