Mental workload of frontline nurses aiding in the COVID-19 pandemic: A latent profile analysis.

Aims: To investigate the mental workload level of nurses aiding the most affected area during the Coronavirus disease 2019 (COVID-19) pandemic and explore the subtypes of nurses regarding their mental workload.

Design: Cross-sectional study.

Methods: A sample of 446 frontline nurses participated from March 8 to 19, 2020. A latent profile analysis was performed to identify clusters based on the six subscales of the Chinese version of the National Aeronautics and Space Administration Task Load Index. The differences among the classes and the variables including sociodemographic characteristics, psychological capital and coping style were explored.

Results: The level of mental workload indicates that the nurses had high self-evaluations of their performance while under extremely intensive task loads. The following three latent subtypes were identified: 'low workload & low self-evaluation' (8.6%); 'medium workload & medium self-evaluation' (35.3%) and 'high workload & high self-evaluation' (56.1%) (Classes 1, 2, and 3, respectively). Nurses with shared accommodations, fewer years of practice, junior professional titles, lower incomes, nonmanagement working positions, lower psychological capital levels and negative coping styles had a higher likelihood of belonging to Class 1. In contrast, senior nurses with higher psychological capital and positive coping styles were more likely to belong to Classes 2 and 3.

Conclusion: The characteristics of the 'low workload & low self-evaluation' subtype suggest that attention should be paid to the work pressure and psychological well-being of junior nurses. Further research on regular training program of public health emergency especially for novices is needed. Personnel management during public health events should be focused on the allocation between novice and senior frontline nurses.

Impact: This study addresses the level of mental workload of frontline nurses who aid in the most severe area of the COVID-19 pandemic in China and delineates the characteristics of the subtypes of these nurses.