دانلود مقاله ISI انگلیسی شماره 24990
عنوان فارسی مقاله

بررسی آسیب های شغلی با روزهای از دست رفته در میان کارگران معدن زغال سنگ روباز از طریق مدل رگرسیون لجستیک

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
24990 2013 7 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Evaluation of occupational injuries with lost days among opencast coal mine workers through logistic regression models
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Safety Science, Volume 59, November 2013, Pages 86–92

کلمات کلیدی
تجزیه و تحلیل حوادث - مدل رگرسیون لجستیک - آسیب های غیر کشنده شغلی - استخراج از معادن زغال سنگ روباز -
پیش نمایش مقاله
پیش نمایش مقاله بررسی آسیب های شغلی با روزهای از دست رفته در میان کارگران معدن زغال سنگ روباز از طریق مدل رگرسیون لجستیک

چکیده انگلیسی

Despite precautions, mining remains the most hazardous occupation, and coal mining is one of the most dangerous industries for non-fatal occupational accidents. Accidents are complicated events with many factors that affect their formation, and statistical evaluation of accident records can produce valuable information that may prevent such accidents. In this study, a logistic regression analysis method was applied to non-fatal occupational injuries from 1996 to 2009 in an opencast coal mine for Western Lignite Corporation (WLC) of Turkish Coal Enterprises (TKI). The accident records were categorized as occupation, area, reason, age, part of body and lost days, and the SPSS package program was used for statistical analyses. Logistic regression analyses were used to predict the probability of accidents that resulted in greater or less than 3 lost workdays. It is found that the job group with the highest probability of exposure to accidents with greater than 3 lost workdays for non-fatal injuries was the maintenance personnel and workers. The employees were primarily exposed to accidents caused by a mining machine, and the lower and upper extremities have the highest probability of exposure to such risks. Finally, an equation for calculating the probability of exposure to accidents with greater or less than 3 lost workdays was derived. Then, the equation was used to determine the important accident risk factors.

مقدمه انگلیسی

Compared with other industries, the mining industry and related energy resource industries are associated with high rates of occupational injuries and fatalities, and mining is one of the most hazardous work environments in many countries around the world (Sari et al., 2009, Groves et al., 2007, Bajpayee et al., 2004 and Donoghue, 2004). Mining is a hazardous profession and considered at war with the unpredictable forces of nature. As a result, the mining industry continues to be associated with a high level of accidents, injuries, and illness (Maiti et al., 2004). Despite the record of progress in reducing mining fatalities and injuries, both the number and severity of mining accidents remain unacceptable (Kecojevic et al., 2007), and the incidence rates are high compared with other industries (Komljenovic et al., 2008). To identify the potential problem areas, it is necessary to investigate the causes of accidents and control exposure of such risks through quantitative analysis of accident data (Maiti et al., 2001). Human factors approaches to system safety have been used to provide greater insights into the causes of accidents and can be applied to the mining context (Lenné et al., 2012). These models of human error in organizational systems take a systems approach (Reason, 2000). Such models have supported the development of several methods of accident investigation and analysis that use error and latent condition classification schemes to provide an analysis of the types of failure involved in accidents. One of the more widely used approaches is the Human Factors Analysis and Classification System (HFACS) (Shappell and Wiegmann, 2000). HFACS describes four levels of failure: (1) Unsafe Acts, (2) Preconditions for Unsafe Acts, (3) Unsafe Supervision, and (4) Organizational Influences (Shappell and Wiegmann, 2004). Reason proposed the “Swiss Cheese” model of human error where four levels of failure are described. Each level influences the next level as seen in Fig. 1 (Shappell and Wiegmann, 2000). Full-size image (46 K) Fig. 1. The “Swiss Cheese” model of human error causation (adapted from Reason, 2000). Figure options Lost workdays in mining industries are valuable indicators for a number of aspects in job safety programs (Coleman and Kerkering, 2007). According to the European Statistics on Accidents at Work (ESAW), the definition of a non-fatal accident at work is “The definition of what constitutes a notifiable work accident ranges from any work accident, whether it results in an interruption of work or not, to a minimum absence of more than three days”. Accidents with greater than 3 days’ absence from work are reported more than accidents with less than 3 days’ absence from work. Only accidents with greater than 3 days’ absence are considered in the ESAW methodology (EUROSTAT, 2001). In this study, based on the ESAW accident definition, a logistic regression method was used for categorical data analysis to predict the probability of accidents with greater or less than 3 lost workdays. Occupational injuries for the Western Lignite Corporation (WLC) of Turkish Coal Enterprises (TKI), which is the primary state body of lignite coal production in Turkey, were examined. The accident records kept by WLC are reliable, detailed, well organized, and cover a long period. The records include the name of the employee, birth date, accident date, accident time, occupation (the job title of the worker), area (accident location), reason (accident type), body parts affected, and days off from work (Sari et al., 2004). The data used herein comprised occupational accidents from 1996 to 2009 in the opencast coal mine for the WLC. The accidents were categorized for occupation, area, reason, age, part of body as well as lost workdays, and the SPSS package program was used for logistic regression analyses.

نتیجه گیری انگلیسی

Non-fatal occupational accidents are frequent in mining, and preventing or decreasing such accidents is only possible through analyses of such accidents and taking precautions given the results of such analyses. When the non-fatal accidents in the Western Lignite Corporation (GLI) of Turkish Coal Enterprises (TKI) were evaluated using a logistic regression analysis method, it was observed that the job groups with the highest probability of exposure to accidents with greater than 3 lost workdays from non-fatal injuries were maintenance personnel and workers. Additionally, employees were most exposed to accidents caused by mining machines. The workshop area has the highest risk of exposure to non-fatal injuries with greater than 3 lost workdays. The part of the body variables with the highest risk were the upper and lower extremities and the age group with the highest probability of exposure to accidents with greater than 3 lost workdays is the 25–34 age group. From evaluating the significant parameters from the analyses together, the maintenance personnel working in the workshop area have the highest probability of exposure to accidents with greater than 3 lost workdays, which affect lower and upper extremities. According to these results, the equipment protecting the lower and upper extremities can be effective in decreasing non-fatal work accidents. Moreover, in the training related to work accidents, the occupational job groups must be considered and educated in the possible risks. This education should include ergonomic hand carrying, careful use of hand tools, working at high, and importance of using personal protective equipment. Within the scope of this study, the logistic regression prediction model was developed to predict the outcome for a new subject. Logistic regression models are flexible and suitable for data that can be grouped categorically. Therefore, if the factors considered are changed, the logistic model will change and, thus, provide valuable information to researchers. When we have a new subject, we can use the logistic model to predict the probability of accidents with greater or less than 3 lost workdays.

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