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

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.