利用推特数据了解新冠肺炎大流行期间护士的情绪动态。

IF 3.4 3区 医学 Q1 MEDICAL INFORMATICS
Health Information Science and Systems Pub Date : 2023-06-23 eCollection Date: 2023-12-01 DOI:10.1007/s13755-023-00228-9
Jianlong Zhou, Suzanne Sheppard-Law, Chun Xiao, Judith Smith, Aimee Lamb, Carmen Axisa, Fang Chen
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引用次数: 0

摘要

护理人员是医疗保健领域最大的学科,自新冠肺炎爆发以来,一直处于新冠肺炎疫情应对的最前沿。然而,新冠肺炎对护理人员的影响在很大程度上是未知的,护士在不同的疫情浪潮中所经历的情感负担也是未知的。传统的方法通常使用基于调查问题的工具来学习护士的情绪,可能不会反映实际的日常情绪,而是反映调查问题特有的信念。社交媒体越来越多地被用来表达人们的想法和感受。本文使用推特数据描述了新冠肺炎大流行期间居住在澳大利亚新南威尔士州的注册护士和实习护士群体的情绪动态。一个新颖的分析框架考虑了情绪、话题、新冠肺炎的发展以及政府公共卫生行动和重大事件,用于检测护士和实习护士的情绪动态。结果发现,注册护士和实习护士的情绪动态与新冠肺炎在不同波次的发展显著相关。这两组人还表现出了与疫情浪潮的规模和相应的公共卫生反应平行的各种情绪变化。该结果具有潜在的应用,例如调整对护理人员的心理和/或身体支持。然而,这项研究有几个局限性,将在未来的研究中加以考虑,例如未在医疗专业群体中进行验证、样本量小以及推文中可能存在的偏见。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Leveraging twitter data to understand nurses' emotion dynamics during the COVID-19 pandemic.

Leveraging twitter data to understand nurses' emotion dynamics during the COVID-19 pandemic.

Leveraging twitter data to understand nurses' emotion dynamics during the COVID-19 pandemic.

Leveraging twitter data to understand nurses' emotion dynamics during the COVID-19 pandemic.

The nursing workforce is the largest discipline in healthcare and has been at the forefront of the COVID-19 pandemic response since the outbreak of COVID-19. However, the impact of COVID-19 on the nursing workforce is largely unknown as is the emotional burden experienced by nurses throughout the different waves of the pandemic. Conventional approaches often use survey question-based instruments to learn nurses' emotions, and may not reflect actual everyday emotions but the beliefs specific to survey questions. Social media has been increasingly used to express people's thoughts and feelings. This paper uses Twitter data to describe the emotional dynamics of registered nurse and student nurse groups residing in New South Wales in Australia during the COVID-19 pandemic. A novel analysis framework that considered emotions, talking topics, the unfolding development of COVID-19, as well as government public health actions and significant events was utilised to detect the emotion dynamics of nurses and student nurses. The results found that the emotional dynamics of registered and student nurses were significantly correlated with the development of COVID-19 at different waves. Both groups also showed various emotional changes parallel to the scale of pandemic waves and corresponding public health responses. The results have potential applications such as to adjust the psychological and/or physical support extended to the nursing workforce. However, this study has several limitations that will be considered in the future study such as not validated in a healthcare professional group, small sample size, and possible bias in tweets.

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来源期刊
CiteScore
11.30
自引率
5.00%
发文量
30
期刊介绍: Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.
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