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Social Exposure and Burnout During the Pandemic: An Investigation on Three Different Positions of Frontline Metro Staffs

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13307))

Abstract

Besides being a threat to public physical health, COVID-19 may also bring harm to peoples’ mental health as well. This preliminary study aimed to explore how different levels of social exposure might result in different mental health outcomes (e.g., burnout) on frontline metro staff, who guarantee the efficiency and safety of urban transportation. Three positions of frontline metro staff with different levels of social exposure, namely station attendants, train drivers, and maintenance workers. Two waves of cross-sectional studies were conducted at two time points, one was shortly after the lockdown in 2020, the other was 5 months later in July 2020. Results showed that there is no significant difference between stress levels after the lockdown. However, a significant difference was observed in the burnout levels after several months of operation. Staff with more contact with passengers (i.e., station attendants) reported the highest level of burnout. Staff with less contact with passengers (i.e., maintenance workers) reported the lowest level of burnout. A possible explanation of such phenomenon was that higher social exposure during the pandemic may cause more anxiety and fear to be infected as well as more emotional labor to deal with people wearing masks. We also discussed possible methods to improve the well-being of metro staff.

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Acknowledgements

Yao Fu and Ranran Li contributed equally to the writing of this paper. This study was supported by the National Natural Science Foundation of China (Grant NO. 52072320)

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Correspondence to Jingyu Zhang .

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Fu, Y., Li, R., Zhang, J., Guo, Z., Feng, G. (2022). Social Exposure and Burnout During the Pandemic: An Investigation on Three Different Positions of Frontline Metro Staffs. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. HCII 2022. Lecture Notes in Computer Science(), vol 13307. Springer, Cham. https://doi.org/10.1007/978-3-031-06086-1_14

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  • DOI: https://doi.org/10.1007/978-3-031-06086-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06085-4

  • Online ISBN: 978-3-031-06086-1

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