2024-03-28T10:25:20Z
https://nagoya.repo.nii.ac.jp/oai
oai:nagoya.repo.nii.ac.jp:02002117
2023-11-16T01:57:08Z
499:508:509:1646019880489
Developing online lectures using text mining reduces health workers’ anxiety in non-epicenter areas of COVID-19
Ogasawara, Masahiko
Uematsu, Haruhiro
Hayashi, Kuniyoshi
Osugi, Yasuhiro
COVID-19
healthcare worker
anxiety
text mining analysis
online lecture
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.
departmental bulletin paper
Nagoya University Graduate School of Medicine, School of Medicine
2022-02
application/pdf
Nagoya Journal of Medical Science
1
84
42
59
0027-7622
2186-3326
https://nagoya.repo.nii.ac.jp/record/2002117/files/05_Ogasawara.pdf
eng
https://www.med.nagoya-u.ac.jp/medlib/nagoya_j_med_sci/841.html
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