Journal of Health and Safety at Work

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Introduction: Manual dexterity impairment due to wearing latex and nitrile gloves among health care employees and laboratory personnel can be a remarkable problem because of its adverse outcomes. The present study was conducted to “compare the effect of using latex and nitrile laboratory gloves on hand dexterity”.

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Material and Method: In a semi experimental study design, 30 university were students randomly selected from Urmia University of medical sciences. Subjects assigned in three experimental conditions, such as the control condition (without gloves), with latex glove and with nitrile gloves. Then, dexterity level of fine finger and gross of the subjects were calculated using the Purdue pegboard test. Repeated measures one-way ANOVA test and T-test and Pearson’s correlation coefficient were used for statistical analysis.

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Results: The result showed that the differences between three groups of laboratory conditions from the level of gross and fine finger dexterity were statistically significant (p‹0.05). As the latex gloves showed significant and positive effect on gross and fine finger dexterity comparable with nitrile gloves and control group but there was no significant difference between the gross and fine finger dexterity of nitrile gloves when comparing with the control group. In other words, the nitrile gloves had not negative effect on gross and fine finger dexterity.

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Conclusion: Considering that there was no significant difference in the score of both fine finger and gross hand dexterity while using nitrile gloves as compared to the control condition (without gloves), means that use of nitrile gloves has no adverse effect on hand dexterity therefore, using nitrile gloves is recommended as a alternative for the latex gloves, considering the additional advantage of no allergic reaction in this gloves.

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Introduction: Heat is one of the hazardous physical agents in the workplace. Exposure to heat and consequent thermal stress influence workers productivity in addition to adverse health effects. The aim of this study was to determine the heat stress induced productivity loss related to different tasks of farmers in Darreh Shahr city, during summer.

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Material and Method: This cross-sectional study was conducted in summer, 2014, among farmers in Darreh Shahr city. After determining the sample size, farmers’ activities were determined using hierarchical task analysis (HTA), and WBGT measurements were done according to the ISO7243. Metabolism was estimated by the ISO8996. Following, the type of activities were identified according their required metabolism. Knowing WBGT and workload and using the work capacity model, the productivity loss in different tasks and ultimately total productivity loss were calculated.

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Result: The mean WBGT activities for plowing, terracing, planting seeds, watering, fertilizing, weeding, spraying, and harvesting were 29.98 °C, 31.28 °C,30.66 °C,31.39 °C,31.99 °C,31.75 °C,31.08 °C, and 30.3 °C, respectively. WBGT values were higher than the permissible level provided by ISO7243 in all farming activities. Maximum value of WBGT was belonged to fertilizing activity (31.99 °C) and the lowest value was for plowing (29.98 °C). ANOVA test results did not show a significant difference in WBGT at head, waist, and ankle height. The highest and lowest amount of productivity loss was estimated respectively for weeding and plowing activities. The total productivity loss for farming was calculated 69.3 percent in an hour which is due to high physical activity, working outdoor, with exposure to direct solar radiation, and consequent heat stress imposed to workers.

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Conclusion: Productivity is a factor which is affected by the workplace heat stress. According to results of the present research, the amount of productivity is reduced in different tasks due to heat and this reduction is exacerbated by increase in temperature and might impact the local economy. Thus, further studies are needed to improve the working conditions.

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Introduction: Correspondence analysis method and preparation of accidents and occupational hazards pattern is able to predict and anticipate accidents and is automatically prioritize the risks and injuries. The aim of this study was to present accidents and occupational hazards pattern based on risk-injury groups, which use it to manage of occupational accidents.

Material and Method: The report of occupational accidents, registered in the social security organization was collected in a period of ten years from 2005 to 2015 (222,300 accidents). Types of risk and injuries to any of the accidents specified based on International Labor Organization criteria and risk of injury were classified in a matrix (18 × 18). Risk-injury groups were separately identified using correspondence analysis and collapse process, as patterns of accidents and occupational hazards. In the mentioned patterns, the relationship between risks and damage can be identified, as it facilitates decision-making in risk assessment in companies covered by the social security organization.

Result: According to the findings, three groups of occupational accidents were obtained and variables of these three groups extracted from the obtained patterns. The first group included six risks and seven injuries that the risks variables were: contact with hot materials, accidents caused by caustic  and corrosive substances, contact with chemicals, accidents caused by toxic substances, contact with electrical equipment, explosion and fire, and injuries were: burns, other injuries, multiple injuries, gas poisoning, suffocation, poisoning, environmental hazards. The second group included seven risks and six injuries that the risks variables were: accidents caused by displacement, projections of fragments or particles, accidents caused by machine tools, slipping, falling people, falling objects, other accidents and injuries were: twists and sprains, dipping the objects in the body, objects in the eyes, cuts and amputations, superficial wounds, deep wounds. Finally, the third group included five risks and five injuries that risks variables were: Falling under the rubble, accident with vehicle, accidents caused by displacement, colliding of persons against objects, projections of fragments or particles, accidents caused by manual tools, trapped between objects, accidents caused by machine tools and injuries were: fractures, dislocation, back pain, hitting, contusions and crushing. It should be noted that the study of these patterns can be used to identify and prioritize of occupational accidents.

Conclusion: The proposed groups make new opportunities for development of the applications to analyze, interpret and automate management of occupational accidents in order to minimize uncertainty and increase its objectivity. Its advantage over other similar analyses can be considering both the risks and injury and to obtain groups of two variables. Due to the frequency and distribution of mass of risk and injury variables in the groups, the risk and injury variables of group 3 are the most important, and the risk and injury variables of group 2 are less important and the risk and injury variables of group 1 have the least importance.

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Introduction: These days, there is a consensus that emotional intelligence plays an important role in the success of individuals in different areas of life. Persons with higher emotional intelligence had lower stress in dealing with demands and pressures in the workplace. The purpose of this study was to use artificial neural network to predict job stress and to compare the performance of this model with the multivariate regression model.
Material and Methods: In order to do that, 892 participants were selected randomly in different job categories. Then, 15 dimensions of Bar-On questionnaire, 10 job categories, age and education were considered as input variables and 7 dimensions of health and safety executive HSE were determined as output variables in models.
Results: The results revealed that an artificial neural network with hyperbolic tangent and sigmoid transfer functions respectively in hidden and output layers with 375 hidden neurons had significantly better performance than multivariate regression. So that, correlation of predicted values and job stress were only between 0.192-0.364 in regression model, but neural network had at least correlation 0.527 in all dimensions of job stress.
Conclusion: In predicting job stress using emotional intelligence, artificial neural network method was much better than multivariate regression model.