Establishment of the norm of Core Occupational Stress Scale for workers of electronic manufacturing industry in China / 中国职业医学

{L-End}Objective To establish the norm of Core Occupational Stress Scale (COSS) for electronic manufacturing industry workers in China. {L-End}Methods A total of 3 049 workers from five electronic manufacturing enterprises in four prefecture-level cities concentrated distribution of the electronics manufacturing industry in China were selected as research subjects using a stratified sampling method. COSS was used to investigate occupational stress levels, and the mean norm, percentile norm and threshold norms were established. {L-End}Results The average score of COSS for the electronic manufacturing industry workers in China was (43.5±7.4) points, and the average scores of social support, organization and reward, demand and effort, and autonomy dimensions were (9.5±3.1), (15.1±3.9), (13.1±3.0), and (5.7±2.0) points, respectively. A total score of 0.0-<47.0 points was determined as no occupational stress, 47.0-<51.0 points as mild occupational stress, 51.0-≤54.0 points as moderate occupational stress, and >54.0 points as severe occupational stress. {L-End}Conclusion The norm of COSS for workers in China's electronics manufacturing industry has been established, which can provide a reference for the evaluation and intervention of their occupational stress levels.

Analysis on influencing factors on occupational stress of medical staff during major public health emergencies / 中国职业医学

Objective To analyze the current status and influencing factors of occupational stress among medical staff during major public health emergencies. Methods A total of 491 medical staff in Guangzhou City was selected as the research subjects using a convenient sampling method. The Job Content Questionnaire and Effort-Reward Imbalance Questionnaire were used to evaluate the occupational stress level in the job demand-control (JDC) and effort-reward imbalance (ERI) models among the staff. Results Among the research subjects, the detection rates of occupational stress in JDC and ERI models were 50.1% and 52.5%, respectively. There was no significant difference in the detection rates of occupational stress between the two models (P>0.05). The result of multivariate logistic regression analysis showed that individuals with lower monthly income and longer weekly working hours had a higher risk of occupational stress in both JDC and ERI models (all P<0.05). Staff with night shift, daily sleep time less than six hours, and dissatisfaction with the protective measures provided by the hospital had higher risk of occupational stress than those with no night shift, daily sleep time at least six hours, and satisfaction with the protective measures provided by the work place in JDC and ERI models (all P<0.01). The risk of ERI model occupational stress of personnel who had been exposed to COVID-19 cases or suspected cases was higher than that in those who had not been exposed (P<0.05). Conclusion Medical staff experience a certain level of occupational stress, characterized by both JDC and ERI models, during major public health emergencies. The main influencing factors included personal monthly income, weekly working hours, night shift, sleep time, satisfaction with protective measures and occupational exposure.