2017
DOI: 10.1016/j.shaw.2017.03.002
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Health Inequalities Among Korean Employees

Abstract: BackgroundSocial status might be a determinant of occupational health inequalities. This study analyzed the effects of social status on both work environments and health outcomes.MethodsThe study sample consisted of 27,598 wage employees aged 15 years and older from among the Korean Working Condition Survey participants in 2011. Work environments included atypical work, physical risks, ergonomic risks, work demands, work autonomy, social supports, and job rewards. Health outcomes comprised general health, heal… Show more

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Cited by 5 publications
(3 citation statements)
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References 30 publications
(33 reference statements)
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“…The aforementioned women who work mainly in the sales and service sectors, were often referred to as “pink collar workers”. Pink collar workers and unskilled workers both have poorer physical and mental health, than those who are not in this category [ 37 , 38 , 39 ]. In addition, service sector workers also have long working hours.…”
Section: Discussionmentioning
confidence: 99%
“…The aforementioned women who work mainly in the sales and service sectors, were often referred to as “pink collar workers”. Pink collar workers and unskilled workers both have poorer physical and mental health, than those who are not in this category [ 37 , 38 , 39 ]. In addition, service sector workers also have long working hours.…”
Section: Discussionmentioning
confidence: 99%
“… 17 According to a study on the health inequality of Korean wage workers, female workers reported a higher risk of work-related musculoskeletal disease than male workers. 18 In 2006, the World Health Organization published an extensive list of recommendations on sex-disaggregated data collection and analysis for occupational exposures, diseases, and injuries. 19 In the ILO report, 20 the lack of sex-disaggregated data makes it more difficult to identify hazards, risks, and types of occupational injuries and diseases that specifically affect each gender, thereby impeding the development of effective OSH policies.…”
Section: Discussionmentioning
confidence: 99%
“…An amazing feature of machine learning is its strong predictive ability. Machine learning has been widely used in various fields [5][6][7][8][9][10][11][12][13][14][15][16]. Support vector machine regression is originally proposed by Vapnik in 1995, and were widely used in all walks of life.…”
Section: Introductionmentioning
confidence: 99%