Showing 3 results for Ahmadi Moshiran
Vahid Ahmadi Moshiran, Ali Karimi, Farideh Golbabaei, Mohsen Sadeghi Yarandi, Ali Asghar Sajediyan, Aysa Ghasemi Koozekonan,
Volume 10, Issue 4 (11-2020)
Abstract
Introduction: Styrene (C₆H₅CH=CH₂) is known as one of the volatile organic substances produced or used in petrochemical industries. Exposure to this chemical compound can thus lead to respiratory diseases. Therefore, this study aimed to evaluate occupational exposure to styrene vapor and to determine the risks of its health consequences in petrochemical industry workers through a quantitative method.
Material and Methods: In this descriptive cross-sectional study, a total number of 150 samples from 50 employees were studied using the National Institute for Occupational Safety and Health (NIOSH) 1501 method. The samples were then analyzed by the Varian CP-3800 chromatograph. Upon determining exposure to styrene vapor, the risk assessment of the health consequences of styrene in the workers in the given industry was evaluated via the relationships established by the United States Environmental Protection Agency (US/EPA).
Results: According to the results, the polybutadiene latex unit (PBL) was observed with the highest average exposure (0.44 mg.(kg-day)-1). Therefore, the top predictors of carcinogenic and non-carcinogenic risks were valued 0.44 and 0.71, respectively, for the PBL unit. Given the lowest average exposure (0.0012 mg.(kg-day)-1) in the drying unit, the prediction revealed the most moderate carcinogenic (0.1 ×10-5) and non-carcinogenic risks (2×10-3) for the same employees.
Conclusion: Overall, the health risk was higher than the permissible limit in the petrochemical industry studied, especially in the PBL unit. Therefore, it is recommended to make use of artificial ventilation, notably the local type, in addition to the natural ones.
Mehran Pourhossein, Reza Pourbabaki, Elahe Roudi, Vahid Ahmadi Moshiran, Homa Maleck Khani, Samane Khodaverdloo,
Volume 10, Issue 4 (11-2020)
Abstract
Introduction: Errors are a byproduct of human information processing or cognitive functioning. Although everyone is disposed to an error while performing various activities, individual differences in cognitive abilities can lead to various types and rates of errors committed in similar situations. Human errors are one of the most important challenges in work environments, including health care systems, wherein such errors are abundantly occurring. Errors in the delivery of correct medications due to the resemblance in appearance and name are thus one of the cognitive errors that come about in health care systems. The main purpose of this systematic review was to evaluate evidence and approaches recently practiced to reduce medication errors caused by the use of look-alike-sound-alike (LASA) medications.
Material and Methods: The study was conducted on August 30, 2018, through searches in the databases of PubMed and Embase, all available years, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) instructions. The searches were done in the titles or abstracts of the articles using the intended terms and the Medical Subject Headings (MeSH) index in combination. These studies were selected based on the inclusion and exclusion criteria and then categorized based on the type of interventions and outputs. Finally, the data were analyzed descriptively.
Results: The research designs and methods varied widely among the studies. There were also discrepancies in the number of participants, number of tests, type of medications, and test conditions. The approaches examined in these studies were tall-man lettering, color-coding, label background variations, and use of signs and symbols. Accordingly, 11 studies had utilized tall-man lettering and the most important reported in all articles were “error rate” and “response times”. As well, a wide range of medication names had been tested. It should be noted that medication
Conclusion: errors have different dimensions, but the errors caused by the look-alike-sound-alike (LASA) medications and the effect of tall-man lettering of medication name were only investigated in the present study. Laboratory studies in this respect have shown that tall-man lettering contributes to mitigating the rate of errors, which might be due to the better legibility of labels, but evaluations in real work environments are needed to reinforce this conclusion. There is also insufficient evidence to support color-coding, as well as several other approaches such as use of signs and symbols. Because of the novelty of the studies in this field, no uniform mechanism has been so far introduced.
Aysa Ghasemi Koozekonan, Vahid Ahmadi Moshiran, Mohsen Sadeghi Yarandi, Hamedeh Golmohammapour, Babak Sarbaz, Seyed Abolfazl Zakerian,
Volume 11, Issue 3 (9-2021)
Abstract
Introduction: One of the most important factors affecting quality of work-life (QWL) is the workload in the form of mental and physical. The increased workload may reduce the level of performance of workers. Therefore, the present study aimed to evaluate the relation of the mental and physicalworkload with the QWLof workers in the foundry industry.
Material and Methods: This cross-sectional study was conducted on 218 male workers in a foundry industry and the work units with high work stress were selected. The NASA-TLX, physical load and the Walton questionnaires were used to collect and evaluate the workload and the QWL.The SPSS software was used for data analysis.
Results: It was clarified that there was a significant inverse relationship between QWL with physical load (P = 0.001) and mental workload (P = 0.01). The comparison of mean QWL between different groups of demographic variables showed that the only significant relationship was between different age groups and QWL (P = 0.003). Finally, it was found that the variables of physical load, mental workload and age had a significant simultaneous effect on the mean score of QWL (P<0.05).
Conclusion: The findings showed that there was a significant relationship between QWL and mental and physical workloads.
