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通过事故学习提高建筑工人的安全绩效。

Improving Safety Performance of Construction Workers through Learning from Incidents.

机构信息

Shenzhen Research Institute of the Hong Kong Polytechnic University, Shenzhen 518057, China.

Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China.

出版信息

Int J Environ Res Public Health. 2023 Mar 4;20(5):4570. doi: 10.3390/ijerph20054570.

DOI:10.3390/ijerph20054570
PMID:36901580
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10002101/
Abstract

Learning from incidents (LFI) is a process to seek, analyse, and disseminate the severity and causes of incidents, and take corrective measures to prevent the recurrence of similar events. However, the effects of LFI on the learner's safety performance remain unexplored. This study aimed to identify the effects of the major LFI factors on the safety performance of workers. A questionnaire survey was administered among 210 construction workers in China. A factor analysis was conducted to reveal the underlying LFI factors. A stepwise multiple linear regression was performed to analyse the relationship between the underlying LFI factors and safety performance. A Bayesian Network (BN) was further modelled to identify the probabilistic relational network between the underlying LFI factors and safety performance. The results of BN modelling showed that all the underlying factors were important to improve the safety performance of construction workers. Additionally, sensitivity analysis revealed that the two underlying factors-information sharing and utilization and management commitment-had the largest effects on improving workers' safety performance. The proposed BN also helped find out the most efficient strategy to improve workers' safety performance. This research may serve as a useful guide for better implementation of LFI practices in the construction sector.

摘要

从事件中学习(LFI)是一个寻求、分析和传播事件严重程度和原因的过程,并采取纠正措施防止类似事件再次发生。然而,LFI 对学习者安全绩效的影响仍未得到探索。本研究旨在确定主要 LFI 因素对工人安全绩效的影响。在中国,对 210 名建筑工人进行了问卷调查。进行了因素分析以揭示潜在的 LFI 因素。进行了逐步多元线性回归分析以分析潜在的 LFI 因素与安全绩效之间的关系。进一步建立了贝叶斯网络(BN)来识别潜在的 LFI 因素与安全绩效之间的概率关系网络。BN 建模的结果表明,所有潜在因素对于提高建筑工人的安全绩效都很重要。此外,敏感性分析表明,两个潜在因素——信息共享和利用以及管理承诺——对提高工人的安全绩效影响最大。所提出的 BN 还有助于找出提高工人安全绩效的最有效策略。这项研究可以为更好地在建筑行业实施 LFI 实践提供有益的指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd6/10002101/932f6b34d149/ijerph-20-04570-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd6/10002101/cf51e7aaeeaa/ijerph-20-04570-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd6/10002101/17cb6fb44132/ijerph-20-04570-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd6/10002101/932f6b34d149/ijerph-20-04570-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd6/10002101/cf51e7aaeeaa/ijerph-20-04570-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd6/10002101/17cb6fb44132/ijerph-20-04570-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd6/10002101/932f6b34d149/ijerph-20-04570-g003.jpg

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本文引用的文献

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Int J Environ Res Public Health. 2020 Jun 9;17(11):4110. doi: 10.3390/ijerph17114110.
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十二个建筑项目的安全管理与安全绩效的对比分析。
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