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Occupational stress and associated risk factors among 13,867 industrial workers in China
OBJECTIVE: Occupational stress is a critical global public health problem. We aimed to evaluate the prevalence of occupational stress among the workers in the electricity, heat, gas, water production and supply (EHGWPS), manufacturing, and transportation industries in Beijing, China. We explored the...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714303/ https://www.ncbi.nlm.nih.gov/pubmed/36466474 http://dx.doi.org/10.3389/fpubh.2022.945902 |
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author | Yan, Tenglong Ji, Fang Bi, Mingli Wang, Huining Cui, Xueting Liu, Baolong Niu, Dongsheng Li, Leilei Lan, Tian Xie, Tingting Wu, Jie Li, Jue Ding, Xiaowen |
author_facet | Yan, Tenglong Ji, Fang Bi, Mingli Wang, Huining Cui, Xueting Liu, Baolong Niu, Dongsheng Li, Leilei Lan, Tian Xie, Tingting Wu, Jie Li, Jue Ding, Xiaowen |
author_sort | Yan, Tenglong |
collection | PubMed |
description | OBJECTIVE: Occupational stress is a critical global public health problem. We aimed to evaluate the prevalence of occupational stress among the workers in the electricity, heat, gas, water production and supply (EHGWPS), manufacturing, and transportation industries in Beijing, China. We explored the demographic differences in occupational stress status among workers in industrial enterprises. METHODS: A cross-sectional study was conducted on 13,867 workers. The self-administered New Brief Job Stress Questionnaire was used to evaluate high occupational stress status, which includes four sub-dimensions (job stressors, stress response, social support, job stressors & social support). Multiple regression and logistic regression models were used to estimate the association between high occupational stress and the four occupational stress sub-dimensions with risk factors. RESULTS: A total of 13,867 workers were included. The prevalence of high occupational stress was 3.3% in the EHGWPS industries, 10.3% in manufacturing, and 5.8% in transportation. The prevalence of high occupational stress was higher than in the other two categories (p < 0.05) in manufacturing industries. Logistic regression analysis showed that male workers with lower educational status, more job experience, and working in manufacturing were vulnerable to high occupational stress. Further analysis of the four occupational stress sub-dimensions showed that male workers, older adult workers, workers with lower educational levels, and longer working time were associated with higher scores in job stressors, stress response, social support, and job stress & social support (all p < 0.05). Moreover, divorced or widowed workers had higher occupational stress scores. CONCLUSION: Male workers with lower educational levels and longer working time may have an increased risk of occupational stress. |
format | Online Article Text |
id | pubmed-9714303 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97143032022-12-02 Occupational stress and associated risk factors among 13,867 industrial workers in China Yan, Tenglong Ji, Fang Bi, Mingli Wang, Huining Cui, Xueting Liu, Baolong Niu, Dongsheng Li, Leilei Lan, Tian Xie, Tingting Wu, Jie Li, Jue Ding, Xiaowen Front Public Health Public Health OBJECTIVE: Occupational stress is a critical global public health problem. We aimed to evaluate the prevalence of occupational stress among the workers in the electricity, heat, gas, water production and supply (EHGWPS), manufacturing, and transportation industries in Beijing, China. We explored the demographic differences in occupational stress status among workers in industrial enterprises. METHODS: A cross-sectional study was conducted on 13,867 workers. The self-administered New Brief Job Stress Questionnaire was used to evaluate high occupational stress status, which includes four sub-dimensions (job stressors, stress response, social support, job stressors & social support). Multiple regression and logistic regression models were used to estimate the association between high occupational stress and the four occupational stress sub-dimensions with risk factors. RESULTS: A total of 13,867 workers were included. The prevalence of high occupational stress was 3.3% in the EHGWPS industries, 10.3% in manufacturing, and 5.8% in transportation. The prevalence of high occupational stress was higher than in the other two categories (p < 0.05) in manufacturing industries. Logistic regression analysis showed that male workers with lower educational status, more job experience, and working in manufacturing were vulnerable to high occupational stress. Further analysis of the four occupational stress sub-dimensions showed that male workers, older adult workers, workers with lower educational levels, and longer working time were associated with higher scores in job stressors, stress response, social support, and job stress & social support (all p < 0.05). Moreover, divorced or widowed workers had higher occupational stress scores. CONCLUSION: Male workers with lower educational levels and longer working time may have an increased risk of occupational stress. Frontiers Media S.A. 2022-11-17 /pmc/articles/PMC9714303/ /pubmed/36466474 http://dx.doi.org/10.3389/fpubh.2022.945902 Text en Copyright © 2022 Yan, Ji, Bi, Wang, Cui, Liu, Niu, Li, Lan, Xie, Wu, Li and Ding. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Yan, Tenglong Ji, Fang Bi, Mingli Wang, Huining Cui, Xueting Liu, Baolong Niu, Dongsheng Li, Leilei Lan, Tian Xie, Tingting Wu, Jie Li, Jue Ding, Xiaowen Occupational stress and associated risk factors among 13,867 industrial workers in China |
title | Occupational stress and associated risk factors among 13,867 industrial workers in China |
title_full | Occupational stress and associated risk factors among 13,867 industrial workers in China |
title_fullStr | Occupational stress and associated risk factors among 13,867 industrial workers in China |
title_full_unstemmed | Occupational stress and associated risk factors among 13,867 industrial workers in China |
title_short | Occupational stress and associated risk factors among 13,867 industrial workers in China |
title_sort | occupational stress and associated risk factors among 13,867 industrial workers in china |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714303/ https://www.ncbi.nlm.nih.gov/pubmed/36466474 http://dx.doi.org/10.3389/fpubh.2022.945902 |
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