Effects of Emotional Labor and Perceived Organizational Support on the Job Attitude of Public Health Workers

2019 ◽  
Vol 13 (1) ◽  
pp. 1-13
Author(s):  
Sun-Hae Shin ◽  
◽  
Jae-Sun Ahn ◽  
Moon-Jung Kim
Author(s):  
Mi-Na Kim ◽  
Yang-Sook Yoo ◽  
Ok-Hee Cho ◽  
Kyung-Hye Hwang

The purpose of this study was to identify the mediating effects of perceived health status (PHS) and perceived organizational support (POS) in the association between emotional labor and burnout in public health nurses (PHNs). The participants were 207 PHNs convenience sampled from 30 public health centers and offices in Jeju, Korea. Data regarding emotional labor, PHS, POS, and burnout were collected between February and March 2021 using a structured questionnaire. Collected data were analyzed by Pearson’s correlation coefficient and multiple regression analysis. Burnout of PHNs was positively correlated with emotional labor (r = 0.64, p < 0.001) and negatively correlated with PHS (r = −0.51, p < 0.001) and POS (r = −0.51, p < 0.001). In the association between emotional labor and burnout, PHS (B = −1.36, p < 0.001) and POS (B = −0.42, p = 0.001) had a partial mediating effect. Reduction of burnout among PHNs requires not only effective management of emotional labor but also personal and organizational efforts to improve PHS and POS.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinghua Li ◽  
Jingdong Xu ◽  
Huan Zhou ◽  
Hua You ◽  
Xiaohui Wang ◽  
...  

ABSTRACT Background Public health workers at the Chinese Centre for Disease Control and Prevention (China CDC) and primary health care institutes (PHIs) were among the main workers who implemented prevention, control, and containment measures. However, their efforts and health status have not been well documented. We aimed to investigate the working conditions and health status of front line public health workers in China during the COVID-19 epidemic. Methods Between 18 February and 1 March 2020, we conducted an online cross-sectional survey of 2,313 CDC workers and 4,004 PHI workers in five provinces across China experiencing different scales of COVID-19 epidemic. We surveyed all participants about their work conditions, roles, burdens, perceptions, mental health, and self-rated health using a self-constructed questionnaire and standardised measurements (i.e., Patient Health Questionnaire and General Anxiety Disorder scale). To examine the independent associations between working conditions and health outcomes, we used multivariate regression models controlling for potential confounders. Results The prevalence of depression, anxiety, and poor self-rated health was 21.3, 19.0, and 9.8%, respectively, among public health workers (27.1, 20.6, and 15.0% among CDC workers and 17.5, 17.9, and 6.8% among PHI workers). The majority (71.6%) made immense efforts in both field and non-field work. Nearly 20.0% have worked all night for more than 3 days, and 45.3% had worked throughout the Chinese New Year holiday. Three risk factors and two protective factors were found to be independently associated with all three health outcomes in our final multivariate models: working all night for >3 days (multivariate odds ratio [ORm]=1.67~1.75, p<0.001), concerns about infection at work (ORm=1.46~1.89, p<0.001), perceived troubles at work (ORm=1.10~1.28, p<0.001), initiating COVID-19 prevention work after January 23 (ORm=0.78~0.82, p=0.002~0.008), and ability to persist for > 1 month at the current work intensity (ORm=0.44~0.55, p<0.001). Conclusions Chinese public health workers made immense efforts and personal sacrifices to control the COVID-19 epidemic and faced the risk of mental health problems. Efforts are needed to improve the working conditions and health status of public health workers and thus maintain their morale and effectiveness during the fight against COVID-19.


2021 ◽  
Author(s):  
Duckhee Chae ◽  
Yunekyong Kim ◽  
Jeeheon Ryu ◽  
Keiko Asami ◽  
Jaseon Kim ◽  
...  

2021 ◽  
Author(s):  
Sarah E. Scales ◽  
Elizabeth Patrick ◽  
Kahler W. Stone ◽  
Kristina W. Kintziger ◽  
Meredith A. Jagger ◽  
...  

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