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  1. C100 医学部/医学系研究科
  2. C100b 紀要
  3. Nagoya journal of medical science
  4. 84(1)

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

https://doi.org/10.18999/nagjms.84.1.42
https://doi.org/10.18999/nagjms.84.1.42
1217f12d-a178-4468-9d8c-166efce12a55
名前 / ファイル ライセンス アクション
05_Ogasawara.pdf 05_Ogasawara.pdf (3 MB)
Item type itemtype_ver1(1)
公開日 2022-02-28
タイトル
タイトル Developing online lectures using text mining reduces health workers’ anxiety in non-epicenter areas of COVID-19
言語 en
著者 Ogasawara, Masahiko

× Ogasawara, Masahiko

en Ogasawara, Masahiko

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Uematsu, Haruhiro

× Uematsu, Haruhiro

en Uematsu, Haruhiro

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Hayashi, Kuniyoshi

× Hayashi, Kuniyoshi

en Hayashi, Kuniyoshi

Search repository
Osugi, Yasuhiro

× Osugi, Yasuhiro

en Osugi, Yasuhiro

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
キーワード
言語 en
主題Scheme Other
主題 COVID-19
キーワード
言語 en
主題Scheme Other
主題 healthcare worker
キーワード
言語 en
主題Scheme Other
主題 anxiety
キーワード
言語 en
主題Scheme Other
主題 text mining analysis
キーワード
言語 en
主題Scheme Other
主題 online lecture
内容記述
内容記述 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.
言語 en
内容記述タイプ Abstract
内容記述
内容記述 This is an Open Access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view the details of this license, please visit (http://creativecommons.org/licenses/by-nc-nd/4.0/).
言語 en
内容記述タイプ Other
出版者
言語 en
出版者 Nagoya University Graduate School of Medicine, School of Medicine
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ departmental bulletin paper
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
ID登録
ID登録 10.18999/nagjms.84.1.42
ID登録タイプ JaLC
関連情報
関連タイプ isVersionOf
識別子タイプ URI
関連識別子 https://www.med.nagoya-u.ac.jp/medlib/nagoya_j_med_sci/841.html
収録物識別子
収録物識別子タイプ PISSN
収録物識別子 0027-7622
収録物識別子
収録物識別子タイプ EISSN
収録物識別子 2186-3326
書誌情報 en : Nagoya Journal of Medical Science

巻 84, 号 1, p. 42-59, 発行日 2022-02
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