Iranian Journal of Ergonomics

Introduction: As workplaces, hospitals are filled with stressors, including environmental and physical stressors (such as noise pollution and poor lighting), human stressors (such as conflicts with colleagues) and organizational stressors (such as heavy workloads and unsuitable work shifts). The inability to cope with these stressors entails physical, psychological and behavioral outcomes for the employees. The present study was conducted to evaluate job stress in nursing personnel and to design a model for assessing the severity of musculoskeletal disorders caused by job stress among nurses.

Materials and Methods: The present cross-sectional analytical study was conducted on 500 nurses working in various teaching hospitals of Hamadan. Data were collected through four questionnaires, including the Job Content Questionnaire, Maslach’s Burnout Inventory, the Visual Analogue Scale and a Demographic Questionnaire. Data were then analyzed in SPSS-16 and LISREL-8.3 using descriptive statistics.

Results: The results of the structural equation modeling showed that job burnout has a mediating effect between the severity of musculoskeletal disorders and job stress. In other words, if factors contributing to job burnout are present, the psychosocial risk factors yielded by the Job Content Questionnaire then contribute to the severity of musculoskeletal disorders.

Conclusion: Given the negative effects of job stress among nurses, health decision-makers are recommended to take measures to reduce stressors such as the physical and psychological demands of the workplace, the lack of support and the lack of job security.

Background and Aim: Occupational accidents are recognized as one of the major concerns in the mining industry. The purpose of this study was to analyze the incidence of occupational accidents in a mine for 10 years using Human Factor Analysis and Classification System (HFACS).
Method: This cross-sectional study was carried out on 664 mining accidents during 2009-2018. The tools used in this study included accident reporting checklists, human factors analysis and classification system (HFACS), and a team approach to analyze these accidents. Data analysis was performed using IBM SPSS AMOS v. 23.0.
Results: The accident frequency rate (AFR) was 15.10±3.34. The results of 10-years accident analysis in this mine based on HFACS model showed that the highest contribution of each parameter to the four layers including unsafe acts, preconditions for unsafe acts, unsafe supervision and organizational influences were respectively devoted to perceptual error (64.4%), Physical environment (29.5%), inadequate supervision (59.6%), and organizational process (65.6%). The results of structural equation modeling showed that the AFR is directly and indirectly affected by the layers of the HFACS model (P<0.05). The most significant impact on the AFR was related to the unsafe acts layer.
Conclusion: The findings of this study indicated that all four causal layers of human factors were effective in mine accidents, in addition the HFACS model is highly effective for unsafe acts-based accidents analysis, so it can be used for future planning to reduce accidents in the mining sector.