Literature DB >> 32571004

Emotional Changes and Protective Factors of Emotional Workers in the Public and Private Sector.

Jongha Lee1, Changsu Han1, Young-Hoon Ko1, June Kang2, Youngmi Byun3, Yeonghae Son3, Ho-Kyoung Yoon1.   

Abstract

OBJECTIVE: Emotional labor is known to be a risk factor for emotional distress. This study aimed to evaluate specific stressors according to the type of occupation and identify protective and adverse factors.
METHODS: We recruited 349 workers engaged in emotional labor in a suburban city. They were assessed using scales regarding emotional status, job stress, resilience, and job satisfaction. Correlation and regression analyses were performed to evaluate their emotional state according to their stress level. A mediation model using structural equation modeling was utilized to identify the mediating effects of resilience and workplace satisfaction.
RESULTS: The correlation analysis indicated that the level of workplace stress was statistically correlated with depressed mood and anxiety and showed a significant inverse correlation with individuals' resilience and job satisfaction. According to the regression analysis, in private institution workers, "emotional disharmony and hurt" had a statistically significant negative effect on their emotional state, and in public institution workers, "emotional demands and regulation" were prominent. Resilience partially mediated the relationship between emotional stress and anxiety/depressive symptoms.
CONCLUSION: Our findings indicate that the causes of stress differed according to the working environment. Preventive strategies such as resilience training and relieving stress on individual factors are needed to promote mental health.

Entities:  

Keywords:  Anxiety; Depressive mood; Emotional labor; Job stress; Resilience

Year:  2020        PMID: 32571004     DOI: 10.30773/pi.2019.0329

Source DB:  PubMed          Journal:  Psychiatry Investig        ISSN: 1738-3684            Impact factor:   2.505


  1 in total

1.  Microblog User Emotion Analysis Method Based on Improved Hierarchical Attention Mechanism and BiLSTM.

Authors:  Xiao Chen; Xiongliang Xiao
Journal:  Comput Intell Neurosci       Date:  2022-06-29
  1 in total

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