A text mining analysis of perceptions of the COVID‐19 pandemic among final‐year medical students

Komasawa, Nobuyasu; Terasaki, Fumio; Nakano, Takashi; Saura, Ryuichi; Kawata, Ryo

AIM: The coronavirus disease 2019 (COVID‐19) pandemic has presented various challenges to medical schools. We performed a text mining analysis via essay task to clarify perceptions among final‐year medical students toward the COVID‐19 pandemic. METHODS: We posed the following essay question to 124 final‐year medical students: “What should medical staff do during the COVID‐19 pandemic; what should you do?” Responses were subjected to quantitative analysis using a text mining approach. Frequently occurring key words were extracted, followed by multidimensional scaling and co‐occurrence network calculations. RESULTS: Of the 124 students, 123 (99.2%) responded to the essay question. The following seven key words were identified as high‐frequency words: medical, infection, patient, human, myself, doctor, and information. Co‐occurrence network calculations revealed that the word “medical” had a high degree of correlation with most key words, except for “doctor.” The word “myself” was correlated with not only “medical” but also “infection,” “human,” and “doctor.” CONCLUSION: Our analysis of perceptions among final‐year medical students toward the COVID‐19 pandemic revealed that most medical students are strongly affected by the COVID‐19 pandemic and are motivated to work as physicians among health care professionals.