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In JMIR nursing

BACKGROUND : Online forums provide a space for communities of interest to exchange ideas and experiences. Nurse professionals used these forums during the COVID-19 pandemic to share their experience and concerns.

OBJECTIVE : The objective of this study is to examine the nurse-generated content to capture the evolution of nurses' work concerns during the COVID-19 pandemic.

METHODS : We analyzed 14,060 posts related to the COVID-19 pandemic from March 2020 to April 2021. The data analysis stage included unsupervised machine learning and thematic qualitative analysis. We used an unsupervised machine learning approach, Latent Dirichlet Allocation (LDA) to identify salient topics in the collected posts. A human-in-the-loop (HITL) analysis complemented the machine learning approach, categorizing topics into themes and sub-themes. We develop insights on nurses' evolving perspective based on temporal changes.

RESULTS : We identified themes for bi-weekly periods and grouped them into 20 major themes based on the work concerns inventory framework. Dominant work concerns varied during the study period. A detailed analysis of patterns in how themes evolve over time enables us to create narratives of work concerns.

CONCLUSIONS : The analysis demonstrates that professional online forums capture nuanced details about nurse work concerns and workplace stressors during the COVID-19 pandemic. Monitoring and assessment of online discussions could provide useful data for healthcare organizations to understand how their primary caregivers are affected by external pressures and internal managerial decisions, and to design more effective responses and planning during crises.

Jiang Haoqiang, Castellanos Arturo, Castillo Alfred, Gomes Paulo J, Li Juanjuan, VanderMeer Debra