Full
Outline
To explore the influencing factors of occupational stress and their relationships among workers in key industries of Bijie City, so as to provide scientific reference for the construction of occupational health intervention system.
A total of 1 986 workers from 8 key industries in Bijie City were enrolled by stratified random sampling. Relevant information on occupational stress was collected by the National Key Population Occupational Health Literacy Monitoring Questionnaire. SPSS 26.0 was used to screen influencing factors, and GeNIe 4.0 was adopted to construct a Bayesian network model for backward reasoning and sensitivity analysis.
The detection rate of occupational stress among workers in key industries of Bijie City was 29.88%.Multivariate analysis showed that females (OR=1.335, 95%CI:1.005-1.774), employees in private enterprises (OR=2.467, 95%CI:1.362-4.469), night shift workers (OR=1.876, 95%CI:1.478-2.382), workers with self-rated general health status (OR=1.710, 95%CI:1.098-2.662), and those lacking occupational health literacy (OR=2.562, 95%CI:2.004-3.276) had an increased risk of occupational stress. The Bayesian network model consisted of 9 nodes and 14 directed edges. Industry category, enterprise nature, weekly working hours, night shift, health status and occupational health literacy were direct influencing factors, while gender was an indirect influencing factor. The classification accuracy of the model was 77.12%, with an AUC of 0.823 (95%CI:0.80-0.85), indicating good predictive efficiency and model fitting performance. Backward reasoning identified 6 core risk nodes, and industry category was the most sensitive variable.
Industry category, enterprise nature, weekly working hours, night shift, health status and occupational health literacy can be taken as the intervention targets for the prevention and control of occupational stress.
237
108
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
