Exploring Nurses’ Online Perspectives and Social Networks During a Global Pandemic COVID-19

Abstract

Objectives. Examine the online interactions, social networks, and perspectives of nursing actors on COVID-19 from conversations on Twitter to understand how the profession responded to this global pandemic.
Design. Mixed methods.
Sample. 10,574 tweets by 2,790 individuals and organisations.
Measurements. NodeXL software was used for social network analysis to produce a network visualisation. The betweenness centrality algorithm identified key users who were influential in COVID-19 related conversations on Twitter. Inductive content analysis enabled exploration of tweet content. A communicative figurations framework guided the study.
Results. Nursing actors formed different social groupings, and communicated with one another across groups. Tweets covered four themes; 1. outbreak and clinical management of the infectious disease, 2. education and information sharing, 3. social, economic, and political context, and 4. working together and supporting each other.
Conclusion. In addition to spreading knowledge, nurses tried to reach out through social media to political and healthcare leadership to advocate for improvements needed to address COVID-19. However, they primarily conversed within their own professional community. Action is needed to better understand how social media is and can be used by nurses for health communication, and to improve their preparedness to be influential on social media beyond the nursing community.

Rights

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. http://creativecommons.org/licenses/by-nc-nd/4.0/

Cite as

Downloadable citations

Identifiers

DOI

https://doi.org/10.1111/phn.12994

Repository URI

https://www.research.ed.ac.uk/en/publications/db401e86-6726-4000-9213-0d94e699a0eb