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TRACEr-RAV(铁路认知失误回溯分析技术):澳大利亚版的可靠性和可用性研究

A reliability and usability study of TRACEr-RAV: the technique for the retrospective analysis of cognitive errors--for rail, Australian version.

机构信息

School of Risk and Safety Sciences, The University of New South Wales, Sydney, NSW 2052, Australia.

出版信息

Appl Ergon. 2011 Nov;42(6):852-9. doi: 10.1016/j.apergo.2011.01.009. Epub 2011 Feb 26.

Abstract

The aim of this study was to compare the usability and reliability of two human error identification tools: TRACEr-Rail (developed by the Rail Safety and Standards Board in the UK) and TRACEr-RAV (an Australian specific version of the tool). Following an attempt to modify TRACEr-Rail to more appropriately suit the Australian rail context, it was predicted that TRACEr-RAV would be rated as more usable and be applied more consistently by Australian users than TRACEr-Rail. In Experiment 1, twenty-five rail employees used either TRACEr-Rail or TRACEr-RAV1 to extract and classify errors from six Australian rail incident reports. In Experiment 2, eleven university students used both TRACEr-Rail and TRACEr-RAV2 to extract and classify errors from three incident summaries. The results revealed that although modification of TRACEr-Rail to become TRACEr-RAV1 and TRACEr-RAV2 did not result in improved inter-rater reliability, modification resulted in improved ratings of usability in Experiment 2. Most participants in Experiment 2 preferred TRACEr-RAV2 to TRACEr-Rail. The poor inter-rater reliability observed was most likely the result of inadequate training, limited practice in using the tools, and insufficient human factors knowledge.

摘要

本研究旨在比较两种人为失误识别工具的易用性和可靠性

TRACEr-Rail(由英国铁路安全与标准委员会开发)和 TRACEr-RAV(该工具的澳大利亚专用版本)。在尝试修改 TRACEr-Rail 以更适应当地铁路环境后,我们预测澳大利亚用户将认为 TRACEr-RAV 比 TRACEr-Rail 更易用且更一致地应用。在实验 1 中,25 名铁路员工使用 TRACEr-Rail 或 TRACEr-RAV1 从六份澳大利亚铁路事故报告中提取和分类错误。在实验 2 中,11 名大学生使用 TRACEr-Rail 和 TRACEr-RAV2 从三份事故摘要中提取和分类错误。结果表明,尽管将 TRACEr-Rail 修改为 TRACEr-RAV1 和 TRACEr-RAV2 并未提高评分者间的可靠性,但在实验 2 中,修改后工具的易用性评分有所提高。实验 2 中的大多数参与者更喜欢 TRACEr-RAV2 而不是 TRACEr-Rail。观察到的评分者间可靠性较差很可能是由于培训不足、使用工具的实践有限以及人为因素知识不足所致。

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