Work-related overexertion injuries in cleaning occupations: An exploration of the factors to predict the days of absence by means of machine learning methodologies

被引：3
作者：
Gonzalez Fuentes, Aroa [1 ]
Busto Serrano, Nelida M. [2 ]
Sanchez Lasheras, Fernando [3 ,4 ]
Fidalgo Valverde, Gregorio [4 ,5 ]
Suarez Sanchez, Ana [4 ,5 ]
机构：
[1] Univ Oviedo, Sch Min Energy & Mat Engn Oviedo, Oviedo, Spain
[2] Minist Labor & Social Econ, Labor & Social Secur Inspectorate, Madrid, Spain
[3] Univ Oviedo, Dept Math, Oviedo 33007, Spain
[4] Univ Oviedo, Inst Univ Ciencias & Tecnol Espaciales Asturias I, Oviedo 33004, Spain
[5] Univ Oviedo, Dept Business Management, Oviedo 33004, Spain
来源：
APPLIED ERGONOMICS | 2022年 / 105卷
关键词：
Work -related overexertion injuries; Absenteeism; Musculoskeletal disorders (MSD); Cleaning sector; Machine learning; MUSCULOSKELETAL DISORDERS; CYANOTOXINS PRESENCE; GENDER-DIFFERENCES; PREVALENCE; RISK; WORKPLACE; HEALTH; SYMPTOMS; JANITORS; VALIDITY;
D O I：
10.1016/j.apergo.2022.103847
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The special characteristics of the cleaning industry have an important impact on the health and safety of its workforce. Making use of data from more than 79,000 occupational accidents, the aim of the present research is to use machine learning techniques to develop a model to predict incapacity for work (expressed in days of absence) due to work-related overexertion injuries among service sector cleaners in Spain. The severity of ac-cidents caused by overexertion depends on several factors that can be classified into the following categories: injury typology, individual factors, employment conditions, accident circumstances and health and safety management and standards in the company.
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页数：9
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