Subtypes of work engagement in frontline supporting nurses during COVID‐19 pandemic: A latent profile analysis

Abstract

AIM: The aim was to examine the subgroups of work engagement in frontline nurses during the COVID-19 pandemic. BACKGROUND: The pandemic may affect the work engagement of nurses who have direct contact with infected patients and lead to a poor quality of care. Identifying classification features of work engagement and tailoring interventions to support frontline nurses is imperative. DESIGN: This study utilized a cross-sectional study design. METHODS: Three hundred fifty-five nurses were enrolled in this cross-sectional study from 14 February to 15 April 2020. A latent profile analysis was performed to identify classification features of work engagement. Multiple logistic regression analyses were used to examine predictors of profile membership. RESULTS: A four-profile model provided the best fit. The four profiles were titled 'low work engagement' (n = 99), 'high vigour-low dedication and absorption' (n = 58), 'moderate work engagement' (n = 63) and 'high work engagement' (n = 135). A regression analysis suggested that young nurses and nurses who were the only children of their family were more likely to be in the 'low work engagement' and 'high vigour-low dedication and absorption' groups. CONCLUSION: This study highlights the importance of tailoring interventions for frontline supporting nurses by considering their distinct work engagement patterns, especially during the COVID-19 pandemic, to improve the promotion of work satisfaction and quality of care. IMPACT: This was the first study to explore the latent profiles of work engagement in frontline nurses during the COVID-19 pandemic. Over 40% of nurses were in the 'low work engagement' and 'high vigour-low dedication and absorption' groups and reported low levels of work engagement. Understanding different patterns of work engagement in frontline nurses can help nursing managers provide emotional, material and organizational support based on the features of each latent profile, which may improve the quality of care and patient safety.

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