OBJECTIVES: Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis. METHODS: The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents. RESULTS: Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers' experience had the strongest influence on the severity of accidents. CONCLUSIONS Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.

OBJECTIVES: Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis.METHODS: The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents.RESULTS: Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers' experience had the strongest influence on the severity of accidents.CONCLUSIONS: Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.
Accidents, Occupational ; Bayes Theorem ; Focus Groups ; Mining ; Occupational Injuries

OBJECTIVES: Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis. METHODS: The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents. RESULTS: Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers’ experience had the strongest influence on the severity of accidents. CONCLUSIONS: Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.
Accidents, Occupational ; Bayes Theorem ; Focus Groups ; Mining ; Occupational Injuries

Background: One of the important actions for enhancing human reliability in any industry is assessing human error probability (HEP). The HEART technique is a robust tool for calculating HEP in various industries. The traditional HEART has some weaknesses due to expert judgment. For these reasons, a hybrid model is presented in this study to integrate HEART with Best-Worst Method.Materials MethodIn this study, the blasting process in an iron ore mine was investigated as a case study. The proposed HEART-BWM was used to increase the sensitivity of APOA calculation. Then the HEP was calculated using conventional HEART formula. A consistency ratio was calculated using BWM. Finally, for verification of the HEART-BWM, HEP calculation was done by traditional HEART and HEART-BWM. Results: In the view of determined HEPs, the results showed that the mean of HEP in the blasting of the iron ore process was 2.57E-01. Checking the full blast of all the holes after the blasting sub-task was the most dangerous task due to the highest HEP value, and it was found 9.646E-01. On the other side, obtaining a permit to receive and transport materials was the most reliable task, and the HEP was 8.54E-04. Conclusion The results showed a good consistency for the proposed technique. Comparing the two techniques confirmed that the BWM makes the traditional HEART faster and more reliable by performing the basic comparisons.

Objectives: The aim of the present study was to investigate the relationships among hospital safety climate, patient safety climate, and safety outcomes among nurses. Methods: In the current cross-sectional study, the occupational safety climate, patient safety climate, and safety performance of nurses were measured using several questionnaires. Structural equation modeling was applied to test the relationships among occupational safety climate, patient safety climate, and safety performance. Results: A total of 211 nurses participated in this study. Over half of them were female (57.0%). The age of the participants tended to be between 20 years and 30 years old (55.5%), and slightly more than half had less than 5 years of work experience (51.5%). The maximum and minimum scores of occupational safety climate dimensions were found for reporting of errors and cumulative fatigue, respectively. Among the dimensions of patient safety climate, non-punitive response to errors had the highest mean score, and manager expectations and actions promoting patient safety had the lowest mean score. The correlation coefficient for the relationship between occupational safety climate and patient safety climate was 0.63 (p<0.05). Occupational safety climate and patient safety climate also showed significant correlations with safety performance. Conclusions Close correlations were found among occupational safety climate, patient safety climate, and nurses’ safety performance. Therefore, improving both the occupational and patient safety climate can improve nurses’ safety performance, consequently decreasing occupational and patient-related adverse outcomes in healthcare units.

OBJECTIVES: Throughout the world, mines are dangerous workplaces with high accident rates. According to the Statistical Center of Iran, the number of occupational accidents in Iranian mines has increased in recent years. This study investigated and analyzed the human and organizational deficiencies that influenced Iranian mining accidents. METHODS: In this study, the data associated with 305 mining accidents were analyzed using a systems analysis approach to identify critical deficiencies in organizational influences, unsafe supervision, preconditions for unsafe acts, and workers' unsafe acts. Partial least square structural equation modeling (PLS-SEM) was utilized to model the interactions among these deficiencies. RESULTS: Organizational deficiencies had a direct positive effect on workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.23). The effect of unsafe supervision on workers' violations and workers' errors was also significant, with path coefficients of 0.14 and 0.20, respectively. Likewise, preconditions for unsafe acts had a significant effect on both workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.21). Moreover, organizational deficiencies had an indirect positive effect on workers' unsafe acts, mediated by unsafe supervision and preconditions for unsafe acts. Among the variables examined in the current study, organizational influences had the strongest impact on workers' unsafe acts. CONCLUSIONS: Organizational deficiencies were found to be the main cause of accidents in the mining sector, as they affected all other aspects of system safety. In order to prevent occupational accidents, organizational deficiencies should be modified first.
Accidents, Occupational ; Humans* ; Iran* ; Mining* ; Models, Statistical ; Organization and Administration ; Systems Analysis

OBJECTIVES: Throughout the world, mines are dangerous workplaces with high accident rates. According to the Statistical Center of Iran, the number of occupational accidents in Iranian mines has increased in recent years. This study investigated and analyzed the human and organizational deficiencies that influenced Iranian mining accidents. METHODS: In this study, the data associated with 305 mining accidents were analyzed using a systems analysis approach to identify critical deficiencies in organizational influences, unsafe supervision, preconditions for unsafe acts, and workers' unsafe acts. Partial least square structural equation modeling (PLS-SEM) was utilized to model the interactions among these deficiencies. RESULTS: Organizational deficiencies had a direct positive effect on workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.23). The effect of unsafe supervision on workers' violations and workers' errors was also significant, with path coefficients of 0.14 and 0.20, respectively. Likewise, preconditions for unsafe acts had a significant effect on both workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.21). Moreover, organizational deficiencies had an indirect positive effect on workers' unsafe acts, mediated by unsafe supervision and preconditions for unsafe acts. Among the variables examined in the current study, organizational influences had the strongest impact on workers' unsafe acts. CONCLUSIONS Organizational deficiencies were found to be the main cause of accidents in the mining sector, as they affected all other aspects of system safety. In order to prevent occupational accidents, organizational deficiencies should be modified first.

OBJECTIVES: Throughout the world, mines are dangerous workplaces with high accident rates. According to the Statistical Center of Iran, the number of occupational accidents in Iranian mines has increased in recent years. This study investigated and analyzed the human and organizational deficiencies that influenced Iranian mining accidents.METHODS: In this study, the data associated with 305 mining accidents were analyzed using a systems analysis approach to identify critical deficiencies in organizational influences, unsafe supervision, preconditions for unsafe acts, and workers' unsafe acts. Partial least square structural equation modeling (PLS-SEM) was utilized to model the interactions among these deficiencies.RESULTS: Organizational deficiencies had a direct positive effect on workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.23). The effect of unsafe supervision on workers' violations and workers' errors was also significant, with path coefficients of 0.14 and 0.20, respectively. Likewise, preconditions for unsafe acts had a significant effect on both workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.21). Moreover, organizational deficiencies had an indirect positive effect on workers' unsafe acts, mediated by unsafe supervision and preconditions for unsafe acts. Among the variables examined in the current study, organizational influences had the strongest impact on workers' unsafe acts.CONCLUSIONS: Organizational deficiencies were found to be the main cause of accidents in the mining sector, as they affected all other aspects of system safety. In order to prevent occupational accidents, organizational deficiencies should be modified first.
Accidents, Occupational ; Humans ; Iran ; Mining ; Models, Statistical ; Organization and Administration ; Systems Analysis