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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
Variables that predict burnout in professional drivers
Patricia Tàpia-Caballero1,2, María-José Serrano-Fernández1,3, Maria Boada-Cuerva2
1Faculty of Education Sciences and Psychology, Universitat Rovira i Virgili, Spain.
Abstract:
Objectives. Stress maintained over time leads to a state of exhaustion known as burnout syndrome. This syndrome constitutes an occupational health problem, leading to high absenteeism. It can also mean that workers come to the workplace feeling unwell, which increases occupational collisions and injuries at work. In this study, we developed a predictive model of burnout in professional drivers using the following indicators: age, hours worked, seniority, educational level, fatigue, personality, attitudes toward driving, safety behaviors in the vehicle, and work characteristics and content. Method. A total of 523 professional drivers from different transport sectors, obtained through non-probability sampling, participated in the study. We used SPSS version 25.0 to analyze the data. Results. We determined the predictive capacity of certain variables that affect drivers and cause burnout. Exhaustion can be predicted with fatigue (48.8%), professional efficiency with emotional stability (39.8%) and cynicism with lack of motivation (28%) as the best predictors. Conclusions. The results contribute to a better knowledge of those factors that cause burnout in professional drivers. It is important to design individual interventions to reduce burnout, which would help reduce sick leave and possible collisions, in addition to providing greater well-being for drivers.
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