Showing 4 results for Nazari
Behzad Karami Matin, Azar Mehrabi Matin, Mansour Ziaei, Zeinab Nazari, Hamed Yarmohammadi, Faramarz Gharagozlou,
Volume 1, Issue 2 (Journal of Ergonomics 2013)
Abstract
Background and aim: the workers in Quarry and Stone Industries done frequently some duties including lifting and carrying of heavy loads and they are exposed to high risk of cumulative trauma disorders. The aim of this study was to comparing the prevalence of musculoskeletal disorders and risk of work postures in Quarry and Stone Industries workers.
Material and methods: this cross-sectional and analytical study was done on 63 workers of Stone Industries and 46 workers of Quarries in Kermanshah. Data were collected using by Nordic standard questionnaire, Body map chart and REBA method. The gathered data was been analyzed by Independent t-test, Mann-Whitney, Spearman and Pearson tests. P was 0.05.
Results: The most prevalence of musculoskeletal disorders in both Stone and Quarry industries was related to low back that was 54% and 39% respectively. Mean and standard deviation of final scores of REBA in Stone and Quarry workers were 9.06±1.45 and 4.6±1.18 respectively. Results shows that a significantly correlation between age, work history and BMI with musculoskeletal disorders in Stone Industries and between age and work history with this disorders in Quarries (P<0.05).
Conclusion: the results shows that the risk level of REBA and prevalence of musculoskeletal disorders of Stone Industries was more than Quarries in shoulder, wrist, hand, lumbar, femur, knee, leg and ankle.
Mansour Ziaei, Hamed Yarmohammadi, Behzad Karamimatin, Soudabeh Yarmohammadi, Zeinab Nazari, Faramarz Gharagozlou,
Volume 2, Issue 2 (Journal of Ergonomics 2014)
Abstract
Background: Nurses’ Burnout can lead to increasing absenteeism and also decreasing energy and quality of service that provided by them. The aim of this study was to determine the prevalence of occupational burnout and its related factors among nurses of a hospital in Kermanshah city in 2013.
Materials and Methods: In this cross-sectional and descriptive-analytical study 189 nurses were selected randomly. Data were collected by demographic questionnaire and Maslach Burnout Inventory (MBI). The gathered data were analyzed by Pearson, Independent T-test and ANOVA in statistical level of p≤0.05.
Results: Mean score of occupational burnout was 61.89. 39.2% nurses reported high emotional exhaustion, 37.6% high depersonalization and 73.5% of them had low personal accomplishment. There were statistical relationship between emotional exhaustion with employment status (p=0.015) and educational degree (p=0.041), between depersonalization with employment status (p=0.022) and working shift (p=0.023), and also between personal accomplishment with age (p=0.010), working experience (p=0.002) and shift working (p=0.045).
Conclusion: The level of nurses’ occupational burnout was average to high. The causes may be high workload, unsafe and boring work environment and high stress. It can be increased the commitment, positive sense to work and personal accomplishment can be reinforced by correct management of human resources including the determination of duties and strategies according to nurses’ ability, new approaches for encouragement and motivation as well as running stress management workshops.
Mrs Laleh Nazari, Dr Azadeh Shahcheraghi, Dr Iraj Etessam,
Volume 9, Issue 3 (Iranian Journal of Ergonomics 2021)
Abstract
Background and Objectives: Job satisfaction and improving employee performance due to direct relationships with individual and environmental factors is one of the most important issues in optimizing the industrial environment. Since the main lever of decreasing or increasing productivity is human resources, one of the issues that will engage the leading managers in the coming decade is trying to increase employee productivity. Regarding the disposal of some affairs to the private sector and lack of adequate health supervision, in some cases, we witness an increase in this disorder in our country. The physical environment of the built environment affects mental health directly and indirectly, meaning that the physical environment, such as the residence and workplace, is a place for human life and some of its psychological effects are unknown.
Methods: The purpose of this research is to explain the model optimization model of labor and the industrial environment through architectural standards. This research is an applied research. In this research, the opinions were gathered through interviews. Then using grounded theory and open, axial and selective coding, the model was developed. In the following, confirmatory factor analysis was used to examine the validity of the items by Lisrel software.
Results: The results showed that indices such as environmental and physical ergonomics, visual contrast of space, environmental psychology, spatial separation of space, attention to voice and color are influential.
Conclusion: Psychological comfort is an inevitable aspect of user satisfaction studies. These findings help designers, architects, planners, and facility managers to develop workplace design principles.
Mousa Nazari, Arezoo Sammak Amani, Mohammad Amin Mououdi, Mohammad Mahdi Alyan Nezhadi,
Volume 11, Issue 4 (Iranian Journal of Ergonomics 2024)
Abstract
Objectives: Work-related musculoskeletal Disorders (WMSDs) are the most significant challenges in both developing and developed countries, affecting the majority of individuals throughout their lives. Considering the detrimental effects of musculoskeletal disorders on the productivity and general health of employees, this research utilizes the Cornell Musculoskeletal Disorder Questionnaire (CMDQ) to develop an intelligent model for assessing and predicting the levels of musculoskeletal disorders.
Methods: In this descriptive-analytical study, 810 employees from five organizations (in four occupational categories, including administrative, technical, production, and services) completed the CMDQ voluntarily. After collecting the questionnaire and performing relevant statistical analyses, data normalization and clustering based on the K-Means method were used to determine levels of musculoskeletal disorders. Finally, the multilayer perceptron artificial neural network was trained to predict the levels of musculoskeletal disorders; moreover, the criteria of precision, accuracy, recall, and F1-score were used to evaluate the proposed model.
Results: The performance of the proposed model in predicting the levels of musculoskeletal disorders is presented in two scenarios (use and non-use of the Synthetic Minority Oversampling Technique (SMOTE) method) based on the evaluation criteria provided. The accuracy, precision, recall, and F1-score values were 0.724, 0.709, 0.756, and 0.720, respectively. The appropriate accuracy and precision in the proposed model indicate its capability to identify the levels of musculoskeletal disorders in individuals and help healthcare professionals take necessary measures to prevent and predict them.
Conclusion: This study employs the CMDQ questionnaire and artificial intelligence to analyze musculoskeletal disorders in the workplace. The proposed model demonstrates significant accuracy and precision compared to similar studies. The results indicate that this model can be utilized to identify and predict musculoskeletal disorders in organizational employees, offering the potential to expedite the identification process and reduce costs.
