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Showing 2 results for nezamodini

Zeinab Sadat Nezamodini, Masume Abasi, Zeinab Mosavianasl, Bahram Kouhnavard,
Volume 2, Issue 3 (July 2018)
Abstract

Background: It is necessary to identify and determine the probability of human error in order to improve the level of health and safety of employees and reduce accidents. For this reason, this study was conducted to identify human error in the flour production process using the "Hazard and Operability” technique. Methods: Data collection was carried out through business case sheets and interviews with workers who have been involved in events as well as affected workers, and through the application of Human Hazop technique. Potential errors of people were predicted, analyzed and the controls were provided. Results: Human Hazop work-sheet analysis showed that the total number of human errors detected in the studied job tasks was 144, 75% of which were eliminated. The results of the study on the causes of the error show that the highest cause of the error is fatigue factor with 33.3%. Conclusion: With the precise application of the Hazard and Operability Method, possible types of operator errors and their consequences can be identified, and control paths to reduce human error can be provided. It canultimately create a safer environment and reduce the number of accidents.
 
Behnoush Jafari, Zeinab Alsadat Nezamodini, Miss Hanan Sari, Saeed Hesam,
Volume 5, Issue 4 (october 2021)
Abstract

Background: Job analysis, detecting hazards, and measuring their relationship with risk perception in workers are efficient ways of preventing accidents. Therefore, the present study is an attempt to identify and assess the risk of job accidents in steel industry in the south of Iran in 2020 using job safety analysis and the William Fine method. The results are also compared with the workers' perception of risk. Methods: The study population consisted of workers in the supplementary section of the studied steel industry(N=169). All the collected data were analyzed in SPSS using frequency and percentage for description and simple/multivariate logistic regression for analysis with sig. equal to 0.05.  To determine the risks, JSA was used. Risk assessment was also performed using William Fine method, and then risk scores were obtained. Afterwards, Risk Perception Questionnaire was used to collect information about risk perception in the workers. Results:In total, 265 job activities along with 2684 risks were identified and evaluated in 7 units of sections in the steel industry. Conclusion: The results of risk assessment and risk perception in this study indicate that when safety risk is properly perceived by workers, the chance of observing safety codes and better detection of risks increases. Therefore, in the face of an unsafe condition at work, workers will be abed to make the right decision and control the risk and prevent work accidents by taking corrective measures and making safe and efficient decisions.

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