Evidence map›Paper›PMID 37359480›Full record

ArticleHealth information science and systems2023

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

Jianlong Zhou, Suzanne Sheppard-Law, Chun Xiao, Judith Smith, Aimee Lamb, Carmen Axisa, Fang Chen

Abstract read
In one paragraph

Article in Health information science and systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jianlong ZhouData Science Institute, University of Technology Sydney, Ultimo, Australia.ORCID 0000-0001-6034-644X
Suzanne Sheppard-LawFaculty of Health, School of Nursing & Midwifery, University of Technology Sydney, Ultimo, Australia.
Chun XiaoResearch Office, University of Technology Sydney, Ultimo, Australia.
Judith SmithFaculty of Health, School of Nursing & Midwifery, University of Technology Sydney, Ultimo, Australia.
Aimee LambFaculty of Health, School of Nursing & Midwifery, University of Technology Sydney, Ultimo, Australia.
Carmen AxisaFaculty of Health, School of Nursing & Midwifery, University of Technology Sydney, Ultimo, Australia.
Fang ChenData Science Institute, University of Technology Sydney, Ultimo, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

COVID-19Mental healthNurseNurse studentTwitter

Identifiers

PMID37359480
PMCPMC10289963

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.