Iranian Journal of Ergonomics

Introduction: Work-related fatigue is a source of concern, even in most industrialized countries. One of the most important factors influencing an employee’s physical and mental condition is the degree to which employees are able to recover from fatigue and stress after work. Factors such as workload can cause fatigue in workers. The aim of this study was using the need for recovery scale to assess workload in mine workers and its relationship with demographics.

Methods: In this cross-sectional study, 80 workers of a mine were surveyed. The data gathering tools used in this study were demographic characteristics questionnaire and the need for recovery scale. The scale assesses the workers workload with 11 two-optioned phrases. Mean, standard deviation, and Pearson’s correlation coefficient and ANOVA tests were used in order for data analysis.

Results: Mean (SD) score of the need for recovery scale was 55.22 (23.93), indicating moderately high workload in the workers. A total of 58.7% of workers experienced high levels of workload. Among the demographics, only body weight had a significant relationship with the need for recovery score (P value = 0.043).

Conclusions: Due to the relatively high need for recovery in the study population, solutions should be employed, such as reducing the workload, use of work-rest schedules, performing heavy tasks within teams, and providing conditions for proper and enough recovery after work, which can increase health conditions of workers.

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.