Emotional labor and job types of male firefighters in Daegu Metropolitan City

Abstract

BackgroundResearch on the emotional labor of firefighters is actively being carried out, but studies that analyze emotional labor separately by job types, working departments, and other conditions of firefighters are insufficient. The purpose of this study is to investigate the difference in emotional labor level between departments and other conditions of firefighters.MethodsA total of 287 Korean firefighters were included for analysis. The demands of emotional labor and emotional damage were measured using the Korean Emotional Labor Scale. To analyze the conditions and factors affecting the emotional labor level of the firefighters, logistic regression analysis was performed using the emotional labor high risk group as a dependent variable.ResultsThe average score of overall emotional labor level of the firefighters was 47.2 ± 17.3, and the prevalence of high risk firefighters with emotional labor was 27.1%. It was found that the emergency medical service and administration departments showed a higher prevalence of high risk emotional labor firefighters than did other departments. The result of multiple logistic regression analysis for the high risk emotional labor firefighters showed that the adjusted odds ratio of emergency medical service department compared to the reference group (the rescue department) was 2.89 (95% confidence interval [CI]: 1.02–8.24).ConclusionsAmong the firefighters, the emergency medical service department is a high risk factor for emotional labor. Therefore, education and prevention training on emotional labor should be more thorough before work shifts.

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

“…The traditional emotion analysis technology is mainly based on single labeling, which cannot analyze a variety of fine-grained emotion information in text data in detail. To solve the recognition of a variety of emotional information contained in the text, it can generally be analyzed as a multilabel classification problem [ 11 – 13 ].…”