잠재프로파일분석을 통한 임금근로자의 위험요인 노출 유형분류 및 영향요인 검증Classifying Latent Profiles in the Exposure to Hazard Factors of Salaried Employees
- Other Titles
- Classifying Latent Profiles in the Exposure to Hazard Factors of Salaried Employees
- Authors
- 이은진; 홍세희
- Issue Date
- 2021
- Publisher
- 한국산업보건학회
- Keywords
- Korean Working Conditions Survey(KWCS); hazard factors in workplace; latent profile analysis; salaried workers
- Citation
- 한국산업보건학회지, v.31, no.3, pp.237 - 254
- Indexed
- KCI
- Journal Title
- 한국산업보건학회지
- Volume
- 31
- Number
- 3
- Start Page
- 237
- End Page
- 254
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/138216
- ISSN
- 2384-132x
- Abstract
- Objectives: This study aims to classify the latent profiles in the exposure to hazard factors of salaried employees and test the determinants.
Methods: Latent profile analysis(LPA) was conducted using data from the fifth Korean Working Conditions Survey(KWCS). 30,050 of salaried employees were the subjects of this study. After classifying the employees, multinomial logistic regression was used to test the determinants.
Results: Salaried employees were classified with three latent profiles based on the exposure to the hazard factors. Employees included in class 1(32.8%) tend to experience low level of physical hazard factors, moderate level of psychological hazard factors, and high level of office work hazard factors. Employees included in class 2(61.8%) tend to be exposed to the moderate to high level of physical hazard factors, moderate to low level of psychological hazard factors, and low level of office work hazard factors. Employees included in class 3(5.4%) tend to experience high level of psychological and physical hazard factors, and moderate level of office work hazard factors. After classification, the demographic, health-, and employmentrelated variables were tested.
Conclusions: This study clarified the features of each class, and proved that employees in class 3 are quite hazardous in that they are exposed to physical and psychological hazard factors much more frequently than other employees. Thus, this study can be used in predicting the high-risk employees and taking preemptive measures for preventing industrial accidents.
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