Abstract
In this research, a model is proposed for predicting the number of days absent from work due to sick or health-related leave among workers in the industry sector, according to ergonomic, social and work-related factors. It employs selected microdata from the Sixth European Working Conditions Survey (EWCS) and combines a genetic algorithm with Multivariate Adaptive Regression Splines (MARS). The most relevant explanatory variables identified by the model can be included in the following categories: ergonomics, psychosocial factors, working conditions and personal data and physiological characteristics. These categories are interrelated, and it is difficult to establish boundaries between them. Any managing program has to act on factors that affect the employees’ general health status, process design, workplace environment, ergonomics and psychosocial working context, among others, to achieve success. This has an extensive field of application in the energy sector.
Highlights
Over the past few years, the main concerns of industry, especially in developed countries, have been to improve the workers’ productivity, occupational health and safety in the workplace, physical and mental well-being, and job satisfaction
Afterwards, we focus on the few works that are oriented to the singularities of the energy sector
It is possible to make use of machine-learning methodologies, such as Multivariate Adaptive Regression Splines (MARS) and genetic algorithms (GA), in order to create models able to predict the number of days of health-related leave among workers in the energy sector
Summary
Over the past few years, the main concerns of industry, especially in developed countries, have been to improve the workers’ productivity, occupational health and safety in the workplace, physical and mental well-being, and job satisfaction. When the industry does not get involved in the abovementioned issues, it can affect the lives of workers. This results in a risk of deterioration in health and causes absenteeism. One of the major concerns is sick leave. N.; Carvalho, F.M.; Lima, V.M.C. Risk factors for absenteeism due to sick leave in the petroleum industry.
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