Journal of Health and Safety at Work

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Introduction: There are some tensions in occupational activities that are caused by harsh conditions and occupational violence is considered one of these tensions. Workplace violence is a global problem. Healthcare workers are globally exposed to the workplace violence more than any other workers. The aim of this study was to investigate the relationship between some individual and job variables with exposure to occupational violence among health care workers in the hospitals in Yazd province, Iran.
 

Material and Method: This cross-sectional, descriptive-analytical study was conducted in eight educational hospitals in Yazd province, Iran. Research population (600 participants) consisted of all healthcare personnel employed in hospitals affiliated to the Shahid Sadoughi University of Medical Sciences in Yazd and were selected by stratified sampling. The Negative Act Questionnaire-Revised (NAQ-R) was used to investigate occupational violence and data analysis was performed using multiple logistic regression tests.
 

Result: The average score of occupational violence was 19.3. Among individual factors, gender (male) and educational level (MSc degree), and among job factors, shift work (morning shift) and type of work environment (genecology and pediatrics wards) had significant relationships with the incidence of violence among the employees.
 

Conclusion: The importance of these findings is in planning and deployment of intervention measures using educational training with regard to the ways of preventing violence, decreasing the coworkers’ aggression, dealing with and increasing awareness about personal and civilian rights in order to reduce the phenomenon of violence among employees.

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Introduction: Occupational back pain is one of the musculoskeletal disorders (MSDs) caused by manual load-lifting among women involved in manual lifting activities. Limits for lifting loads are used to assess the risk and prevention of occupational back pain. The Washington Industrial Safety and Health Act (WISHA) checklist is used as a permissible load assessment limit in Iran. The present study aimed to evaluate the reliability of using the WISHA checklist to determine the allowable limits of manual load-lifting among women based on biomechanical and psychophysical methods.
Material and Methods: In this study, ten women workers aged 20-30 years with a history of manual load-lifting were asked to perform 21 load-lifting tasks designed according to the permitted limits of Iran. A wireless electronic clinometer was used to determine the flexion angles of the trunk while lifting the load. Anthropometric information, load weight, trunk angle, and posture were entered into 3DSSPP software to obtain biomechanical forces for each task and compared them with the recommended National Institute for Occupational Safety and Health (NIOSH) limits. After performing each task, the rate of perceived exertion of each person for each task was collected by using the Borg scale.
Results: The flexion angle for 47.61% of the lifting tasks were more than 20 degrees. The estimated average compressive force for the six tasks exceeded NIOSH’s recommended limits. The average Borg scale was determined hard for 33.33% of lifting tasks and hard to very hard for 9.52% of tasks.
Conclusion: It was found that the manual load-lifting standard for some tasks might not be sufficient to control and prevent low back pain caused by manual lifting in women, and some non-compliance was found with the allowable lifting limits. Manual load-lifting with anthropometric and biomechanical features may increase the biomechanical force on the women’s low back. Therefore, it seems that the allowable lifting limits of Iran for women based on the WISHA checklist should be reviewed and redesigned based on anthropometric and biomechanical characteristics.

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Introduction: Physical fatigue is one of the major risk factors for work-related musculoskeletal disorders and has many life and financial costs. The impact of physical/biomechanical, psychosocial, environmental, and individual risk factors on muscle fatigue is undeniable. The aim of this study is to model the phenomenon of muscle fatigue (as output) in the hand in work environments based on these risk factors (as input) using soft computing methods.
Material and Methods: In the first step, associated risk factors of fatigue for 156 subjects (in three job categories) were assessed using Copenhagen environmental, psychosocial, demographic, and Man-TRA tools. Then, the Roman-Liu equation and mean square amplitude of acceleration waves were used to measure fatigue with a dynamometer and a three-axis accelerometer, respectively. Finally, according to the nature of risk factors and the phenomenon of fatigue, six categories (24 methods) of supervised machine learning (SML) based on classification were selected. MatLab software (MatLab R2017b, The Mathworks Inc., MA, U.S.A.) was used to fit the models using SML.
Results: The best-fitted models in the first and second half of the work shift were obtained using support vector machine methods. Physical risk factors had a significant impact on physical fatigue. After filtering low-priority risk factors, in the first half of the work shift, the most optimal model had an accuracy of 71.8%, precision of 72.5%, sensitivity of 76.9%, specificity of 70.8%, and discrimination power equal to 73%. In the second half of the work shift, the accuracy, precision, sensitivity, and specificity of the optimized model were 60.3%, 57.5%, 50%, and 46.9%, respectively, and the discrimination power was obtained at about 62%.
Conclusion: The fitted models for hand fatigue had acceptable performance in both sections of the shift but can still be optimized. Therefore, it is necessary for future studies to improve the quality of input and output data and include other dimensions affecting fatigue such as cognitive workload and type of work shift in future models.

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Introduction: Biomechanical risk factors, including wrist and elbow angle and standing and sitting position, are the primary factors affecting hand grip strength and perceived exertion. The interaction of these factors can include different effects on grip strength and perceived exertion. Therefore, the present study examines the interaction of varying wrist and elbow angles in sitting and standing postures on the variability of grip strength and perceived exertion.
Material and Methods: In the present study, 30 students (15 females, 15 males) aged 19-30 participated. The average grip strength was measured by a dynamometer for 12 different positions based on the angle of the wrist and elbow in both standing and sitting postures. Perceived exertion was also assessed using the Borg CR-10 scale for each setting. Data analysis was performed using SPSS 23. The Wilcoxon test was applied to compare perceived exertion between standing and sitting postures. Furthermore, a three-way ANOVA was performed to examine interactions between posture (standing/sitting), elbow, and wrist angles. Mauchly’s Sphericity Test was applied to confirm the ANOVA assumptions, and effect sizes for multivariate analysis were calculated (partial η2).
Results: In both sitting and standing postures, the highest average grip strength was observed at a 0-degree wrist angle with a 90-degree elbow angle (standing: 28.6 ± 10.8, sitting 25.8 ± 9.8), while the lowest average grip strength was recorded at full wrist extension with a 0-degree elbow angle (standing: 19.3 ± 6.5, sitting 17.9 ± 6.9). In all three elbow angles examined (0°, 60°, and 90°), the highest value of perceived exertion was recorded in the full extension of the wrist. The changes in the grip strength at different angles were the same for sitting and standing postures, yet the hand grip strength was higher in the standing than the sitting posture (P-value<0.001). The effect of each of the studied factors (sitting and standing postures, wrist angle, and elbow angle) alone on hand grip strength and perceived exertion was deemed to be significant (P-value<0.001). Regarding the two-way interactions of the research variables, the interaction effect of elbow and wrist angles on grip strength (partial η2=0.09, P-value=0.015) and perceived exertion was significant (partial η2=0.08, P-value=0.06). Furthermore, findings indicated that the wrist angle had a more pronounced effect on the value of the perceived exertion (partial η2=0.31, P-value<0.001).