مجله مهندسی بهداشت حرفه ای
سال هشتم شماره 2 (تابستان 1400)

Measuring physical work capacity is one of the important topics in work physiology. It is done with the aim of examining the fit between the work and worker. Body composition components are among the factors influencing the maximum aerobic capacity.They need to be determined using direct and instrumental methods and it is necessary to provide a model to predict the estimated  physical work capacity based on the results of these methods.

 In this cross-sectional study, 50male students were randomly selected. To estimate the maximal aerobic capacity, Bruce protocol and a treadmill were used. Subjects’ body composition components were measured using an instrument during the test. Pearson correlation coefficient and regression analysis were used to examine the relationship between the variables. Finally, a model was presented to predict maximal oxygen consumption based on linear regression analysis.

The mean of age of students was21.36±2.08 years. Mean of maximal aerobic capacity of students was estimated at 44.01±4.75 (ml/kg/Min). The results of present study revealed a significant and negative relationship between maximal aerobic capacity and body composition components.

Since estimating the physical work capacity requires relationships and equations that have acceptable accuracy and validity, a model is presented in this regard based on the results of the present study. The variables of body mass index, body fat percentage and waist-hip ratio play an effective role in estimating physical work capacity and the maximal oxygen consumption can be estimated with high accuracy based on the presented model.

Systematic analysis of accidents in sociotechnical system is very important to reduce accidents. The present study aimed to analyze the relationships between human factors in mining accidents by using a systematic approach. 

The accidents occurred in one of the largest mines in Iran that occurred during a period of 7 years (2013-2019) were collected. All contributing factors in each accident were identified using an accident analysis team by Human Factors Analysis and Classification System (HFACS) framework. A total of 154 accidents were collected. The data were analyzed using logistic regression in SPSS Software, version 16.

Skill-based errors were the most frequently cited at unsafe acts level. Also, the physical environment, planned inadequate operations, and organizational climate were the most frequently identified deficiency in all accidents. Throughout the overall system components, several factors were determined to the presence of deficiency in other components of system; including organizational climate and inadequate supervision, organizational processes and planned inappropriate operations, inadequate supervision and physical environment, inadequate supervision and physical environment.

Skill-based errors, supervision problems, and organizational processes are the most common deficiency in mining industry accidents. Analysis of the relationship between causal factors at different levels of HFACS is to adopting appropriate strategies to reduce the accidents.

In todaychr('39')s world, for many reasons, accidents occur in many organizations. One of the goals of these events is to hit countrieschr('39') industrial bases. To minimize the risk of such threats, it is necessary to design and implement risk identification and risk assessment programs. The purpose of this study was to evaluate the risks associated with conventional and fuzzy FMEA-aware threats and compare the results of these two methods. M

Methods and Materials:

In this study fuzzy FMEA and FMEA methods were used to identify and evaluate hazards. TOPSIS method was used to prioritize the two techniques. Data were analyzed using MATLAB software.

The results showed that in the fuzzy FMEA method, despite the high time and cost of implementation and higher educational needs, due to the high level of accuracy of the results, it is possible to more accurately estimate the risk levels and thus to prioritize the units more efficiently. Therefore, the output of this study introduced the fuzzy FMEA approach as the superior approach.

It was found that using a fuzzy approach can lead to a reduction of uncertainty. This increases the efficiency and thus the efficiency of the organizations

One of the ways to increase the safety factor can be based on the analysis of system resilience. The present study aimed to develop a model for assessing safety resilience in process industries with a passive defense approach based on the Delphi method.

This cross-sectional study was conducted in phase 19 of South Pars in 2018-2020.
This Delphi study was conducted in three rounds with the participation of 18 experts in the fields of chemical and process engineering, safety, occupational health and environment.

After three rounds of Delphi study, the safety resilience assessment model was developed based on the three components of preparedness, likelihood and consequence parameters. The findings showed that experimental data, technical inspection and professional competence of individuals were among effective parameters in likelihood component, parameters of human damage, property damage, process damage, environmental damage and strategic and defense damage were among the effective parameters in the consequence component and finally the parameters of hardware preparedness, software preparedness, defense preparedness and external resources were identified as effective parameters in the preparedness component. In this Delphi study, all members of the expert panel confirmed the items in the algorithm, including resilience components, existing sub-indices with 75% acceptance level.

Based on the expert panel’s opinions, the results of this Delphi study indicated that this semi-quantitatively model has good reliability for evaluating safety resilience in process industries. Therefore, the use of this model can help to give an acceptable estimate of safety resilience in the process industry.

The importance of examining shift work, especially in the health sector, is one of the harmful and irreparable consequences that is attributed to it. Shifts, especially night shifts, lead to fatigue and consequent adverse effects on health, alertness and performance. The aim of this study was to investigate shift work and mental workload on fatigue of intensive care unit nurses.

In this analytical and cross-sectional study, 144 nurses of intensive care units in three hospitals of Shiraz participated by census sampling. The SP (Samn-Perelli) Mental Fatigue Scale and NASA Mental Workload were completed for 2 weeks at the beginning and end of each shift.

The results showed that the amount of mental fatigue and mental workload were significantly associated with the type of work shifts (P <0.05). Also, between the average hours of sleep during the last two weeks (r = -0.24, P <0.01), the average mental fatigue and mental workload (P <0.01, r = 0.25), lack of leisure time (P <0.01, r = -0.22), and having a regular exercise program (P <0.01, r = -0.3), were significantly associated with mental fatigue.

The mental workload and shift schedule affect the fatigue of intensive care unit nurses. But there seem to be other factors that affect nurseschr(chr(chr('39')39chr('39'))39chr(chr('39')39chr('39'))) fatigue. Further studies are needed to investigate the effect of lifestyle and personal factors and its effect on fatigue.

Investing in health and safety has long been considered costly and inevitable, and the cost factor has always been a barrier to implementing safety and health interventions. Decision-makers in this area must demonstrate that such interventions are not only effective in achieving their goals but also have high economic efficiency. The present study aimed to determine the optimal model for economic evaluation in occupational safety and health investments.

The Analytic Hierarchy Process (AHP) method and Expert Choice® software were used to determine the optimal model. First, the OSH economic evaluation models/ tools were identified based on a review of previous studies. Then, considering 7 main criteria, the models were evaluated and the optimal model was introduced.

By literature search and screening of the findings, 20 models/tools were identified. then, using a decision matrix, modelschr(chr(chr(chr('39')39chr('39'))39chr(chr('39')39chr('39')))39chr(chr(chr('39')39chr('39'))39chr(chr('39')39chr('39')))) specifications were extracted and compared with each other. 4 models were identified for prioritization with AHP. The accuracy and precision with a final weight of 0.490 and the comprehensiveness with a final weight of 0.076 were the most and least important criteria, respectively. After prioritizing the alternatives over the target, the ROHSEI model, ToolKit method, Productivity Assessment tool, and Potential Method weighed 0.320, 0.281, 0.240, and 0.159, respectively.

HSE professionals can use ROHSEI as a tool for demonstrating the financial incentives for their goals, allocating limited resources, and improving the economic efficiency of their projects.

The outbreak of Covid-19 has caused a lot of stress and anxiety among people in different communities. Translation results In Iran, as in other countries, the outbreak of this virus has led to many psychological consequences for various segments of society, including nurses. The aim of this study was to evaluate the level of job stress in nurses exposed to covid-19 in Ahvaz educational hospitals.

This case-control study was conducted on 335 nurses in two sections:  a target group (exposed to covid-19 patients) and a control group (not exposed to covid-19 patients). The Osipow questionnaire was used to evaluate job stress. Appropriate statistical analyzes were conducted in a significance level of 0.05.

Job stress with a mean of 190.59 ± 22.56 and 183.07± 21.52, respectively, had a significant mean difference between two groups (target and control) (p <0.05) so that job stress in the target group was higher than the control group (p <0.05). The mean of all components of job stress was significantly different between two groups (p <0.05).Also, the mean of job stress with gender variables, physical activity and employment status was significant (p <0.05).

Due to the increase in job stress score that caused by exposing to covid-19, the availability of effective prevention or treatment strategies to deal with COVID-19, strengthening the psychological services system and also appropriate planning to control demographic factors affecting  job stress, including physical activity and improving the employment status of nurses are recommended.