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- W3025010113 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." @default.
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- W3025010113 date "2020-05-14" @default.
- W3025010113 modified "2023-09-26" @default.
- W3025010113 title "Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms" @default.
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- W3025010113 doi "https://doi.org/10.3390/en13102475" @default.
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