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Occupational accident-precursors data collection and analysis according to Human Factors Analysis and Classification System (HFACS) taxonomy

机译:根据人为因素分析和分类系统(HFACS)分类法对职业事故前兆数据进行收集和分析

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摘要

Data were collected in an automotive production plant during a campaign of observations performed by safety experts. A period of one week of observations was done during which safety experts monitored the working activity of an assembly line. All accident-precursors identified were reported in a format and immediately analysed and classified according to HFACS. Each collected element was classified in 3 categories as: unsafe acts (related to human behaviour), unsafe condition (related to the working condition and working organisation) and near miss (a situation that involved workers without physical consequence for them). Then each element was classified according to the four levels of HFACS: individual factor (violation or error), environmental factor, supervision and organisational factor. This step was supported by short interview with workers and/or supervisors involved to better identify the characterising factors of the event. This survey allowed the identification and classification of 100 accident-precursors that could be used in the company where they have been collected and, more in general, in manufacturing companies, to identify behaviours and areas of improvement for health and safety based on more recurrent factors that characterised the observed events, according to the methodology described in Baldissone et al. .
机译:在安全专家进行的一系列观察活动中,数据是在汽车生产厂中收集的。进行了为期一周的观察,期间安全专家监视了装配线的工作活动。所有识别出的事故前兆均以格式报告,并根据HFACS立即进行分析和分类。每个收集到的要素都分为3类:不安全行为(与人类行为有关),不安全状况(与工作状况和工作组织有关)和差点未命中(这种情况涉及工人,对他们没有身体后果)。然后根据HFACS的四个级别对每个要素进行分类:个人因素(违反或错误),环境因素,监督和组织因素。通过与有关工人和/或主管进行简短访谈,可以更好地确定事件的特征因素,从而支持了这一步骤。这项调查可以对100个事故前兆进行识别和分类,这些事故前兆可以在收集它们的公司中使用,并且更普遍地在制造公司中使用,可以根据更多的经常性因素来识别行为和需要改进的健康与安全领域。根据Baldissone等人描述的方法对观察到的事件进行了特征描述。 。

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