Developing online lectures using text mining reduces health workers’ anxiety in non-epicenter areas of COVID-19

Masahiko Ogasawara, Haruhiro Uematsu, Kuniyoshi Hayashi, Yasuhiro Osugi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


COVID-19 is indirectly associated with various mental disorders such as anxiety, insomnia, and depression, and healthcare professionals who treat COVID-19 patients are particularly prone to severe anxiety. However, neither the anxiety of healthcare workers in non-epicenter areas nor the effects of knowledge support have been examined thus far. Participants were 458 staff working at the Toyota Regional Medical Center who completed a preliminary questionnaire of their knowledge and anxiety regarding COVID-19. Based on text mining of the questionnaire responses, participants were offered an online lecture. The effect of the lecture was analyzed using a pre and post-lecture rating of anxiety and knowledge confidence, and quantitative text mining. The response rates were 45.6% pre and 62.9% post-lecture. Open-ended responses regarding anxiety and knowledge were classified into seven clusters using a co-occurrence network. Before the lecture, 28.2%, 27.2%, and 20.3% of participants were interested in and anxious about “infection prevention and our hospital’s response,” “infection and impact on myself, family, and neighbors,” and “general knowledge of COVID-19,” respectively. As a result of the lecture, Likert-scale ratings for anxiety of COVID-19 decreased significantly and knowledge confidence increased significantly. These changes were confirmed by analyses of open-ended responses about anxiety, lifestyle changes, and knowledge. Positive changes were strongly linked to the topics focused on in the lecture, especially infection prevention. The anxieties about COVID-19 of healthcare workers in non-epicenter areas can be effectively reduced through questionnaire surveys and online lectures using text mining.

Original languageEnglish
Pages (from-to)42-59
Number of pages18
JournalNagoya journal of medical science
Issue number1
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Medicine(all)


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