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Effective care training for patients with COVID-19 through social network
Interactive Learning Environments ( IF 4.965 ) Pub Date : 2021-02-08 , DOI: 10.1080/10494820.2021.1875003
Yaser Sobhanifard 1 , Behnam Soltanmohammadi 1
Affiliation  

ABSTRACT

This study explores effective care training factors for patients with coronavirus disease 2019 (COVID-19) through social network message combining qualitative and quantitative methods. The research was conducted in two phases. In the first phase, based on the theoretical saturation approach, active social networking audiences were sampled and questioned in the field of COVID-19 learning. In this regard, 38 audiences interviewed and based on thematic analysis 20 non-repetitive features (basic them), five organizing themes, and a thematic network extracted. In the second phase, 11 experts were used to rank these extracted basic and organizing themes using the DEMATEL as a quantitative method. Finally, the results were tested using a one-sample T-test and Spearman correlation by a more significant number of social network users. The thematic analysis results introduce 20 basic themes, five organizing themes, and network themes for effective care training factors for patients with COVID-19 through the social network. Indeed, it can be concluded that the performed network themes and DEMATEL entails a unique model in this context. This model shows useful messages for COVID-19 training in the social network under the influence of five factors: simplicity, multimedia, validity, availability, and generalization.



中文翻译:

通过社交网络对 COVID-19 患者进行有效的护理培训

摘要

本研究通过社交网络消息结合定性和定量方法,探索 2019 年冠状病毒病(COVID-19)患者的有效护理培训因素。该研究分两个阶段进行。在第一阶段,基于理论饱和方法,对活跃的社交网络受众进行了抽样调查,并在COVID-19学习领域进行了询问。对此,采访了38位受众,并根据主题分析提炼出20个非重复特征(基本特征)、5个组织主题以及一个主题网络。第二阶段,11位专家使用DEMATEL作为定量方法对这些提取的基本主题和组织主题进行排序。最后,由更多的社交网络用户使用单样本 T 检验和 Spearman 相关性对结果进行了测试。主题分析结果介绍了 20 个基本主题、5 个组织主题和网络主题,通过社交网络为 COVID-19 患者提供有效的护理培训因素。事实上,可以得出结论,所执行的网络主题和 DEMATEL 在这种情况下需要一个独特的模型。该模型在五个因素的影响下显示了社交网络中的 COVID-19 培训的有用消息:简单性、多媒体、有效性、可用性和泛化性。

更新日期:2021-02-08
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