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Objectives. Several studies have shown that one of the most common causes of collision is driver fatigue since fatigue causes drowsiness while driving and this decreases the driver's ability to maneuver the vehicle and increases the probability of their nodding off and falling asleep at the wheel. This may be due to a variety of personal reasons and specific factors connected to working conditions. In the present work we therefore intend to develop a predictive model for fatigue in professional drivers using the following indicators: age, gender, personality, burnout, characteristics and job content. Method. The participants were 516 professional drivers from different transport sectors, obtained through non-probabilistic sampling. SPSS version 25.0 was used for data analysis. Results. The predictive capacity of a number of variables that affect drivers by causing fatigue is determined. Fatigue can be predicted through certain variables, with the best predictor being exhaustion (48.8%). Conclusions. This research contributes to a greater knowledge of the factors that produce fatigue in professional drivers. It highlights the importance of designing interventions to reduce the incidence of fatigue, resulting in greater well-being for the driver and a lower incidence of collisions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives. Professional drivers drive for many hours without rest. This factor, in addition to the characteristics of the job, the vehicle, the environment and the driver, causes driver fatigue. Fatigue is one of the most common risk factors when driving because it causes drowsiness, decreases drivers' attention and may make them fall asleep at the wheel. In this article we propose a predictive model for professional drivers using the following variables: age, number of children, time spent at work, time spent inside the vehicle, personality, job characteristics (JDS), job content (JCQ) and burnout. Method. Participants were 509 professional drivers from various transport sectors recruited by non-probabilistic sampling. SPSS version 25.0 was used for statistical analysis. Results. The predictive capacity of variables that cause driver fatigue was determined. Exhaustion best predicts fatigue positively, while openness to experience best predicts it negatively. Burnout and certain personality characteristics are good predictors, whereas other variables, such as JCQ and JDS, are weak predictors. Conclusions. This study extends our knowledge of the factors that cause fatigue in professional drivers and underlines the importance of designing interventions aimed at reducing the incidence of fatigue, promoting greater driver well-being and lowering the incidence of accidents. [ABSTRACT FROM AUTHOR]