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

被引:0
作者
Jianlong Zhou
Suzanne Sheppard-Law
Chun Xiao
Judith Smith
Aimee Lamb
Carmen Axisa
Fang Chen
机构
[1] University of Technology Sydney,Data Science Institute
[2] University of Technology Sydney,Faculty of Health, School of Nursing & Midwifery
[3] University of Technology Sydney,Research Office
来源
Health Information Science and Systems | / 11卷
关键词
Mental health; Nurse; Nurse student; COVID-19; Twitter;
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摘要
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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