Assessing medical students' perceived stress levels by comparing a chatbot-based approach to the Perceived Stress Questionnaire (PSQ20) in a mixed-methods study. (November 2022)

• Record Type: Journal Article
• Authors: Moldt, Julia-Astrid; Festl-Wietek, Teresa; Mamlouk, Amir Madany; Herrmann-Werner, Anne
• Is Part Of: Digital health. Volume 8(2022)
• Publication Date: 2022-11
• Subjects: Medical students -- stress -- communication -- mixed-methods design -- conversational agent -- PSQ20; Medical care -- Data processing -- Periodicals; Medical informatics -- Periodicals; 362.10285
• DOI: 10.1177/20552076221139092
• Languages: English
• ISSNs: 2055-2076

Abstract

Objective: Digital transformation in higher education has presented medical students with new challenges, which has increased the difficulty of organising their own studies. The main objective of this study is to evaluate the effectiveness of a chatbot in assessing the stress levels of medical students in everyday conversations and to identify the main condition for accepting a chatbot as a conversational partner based on validated stress instruments, such as the Perceived Stress Questionnaire (PSQ20). Methods: In this mixed-methods research design, medical-student stress level was assessed using a quantitative (digital- and paper-based versions of PSQ20) and qualitative (chatbot conversation) study design. PSQ20 items were also shortened to investigate whether medical students' stress levels can be measured in everyday conversations. Therefore, items were integrated into the chat between medical students and a chatbot named Melinda. Results: PSQ20 revealed increased stress levels in 43.4% of medical students who participated ( N = 136). The integrated PSQ20 items in the conversations with Melinda obtained similar subjective stress degree results in the statistical analysis of both PSQ20 versions. Qualitative analysis revealed that certain functional and technical requirements have a significant impact on the expected use and success of the chatbot. Conclusion: The results suggest that chatbots are promising as personal digital assistants for medical students; they can detect students' stress factors during the conversation. Increasing the chatbot's technical and social capabilities could have a positive impact on user acceptance.