Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105794
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building and Real Estate | - |
dc.contributor | College of Professional and Continuing Education | - |
dc.creator | Chan, APC | - |
dc.creator | Guan, J | - |
dc.creator | Choi, TNY | - |
dc.creator | Yang, Y | - |
dc.creator | Wu, G | - |
dc.creator | Lam, E | - |
dc.date.accessioned | 2024-04-23T04:31:21Z | - |
dc.date.available | 2024-04-23T04:31:21Z | - |
dc.identifier.issn | 1661-7827 | - |
dc.identifier.uri | http://hdl.handle.net/10397/105794 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Chan APC, Guan J, Choi TNY, Yang Y, Wu G, Lam E. Improving Safety Performance of Construction Workers through Learning from Incidents. International Journal of Environmental Research and Public Health. 2023; 20(5):4570 is available at https://doi.org/10.3390/ijerph20054570. | en_US |
dc.subject | Bayesian network | en_US |
dc.subject | Construction industry | en_US |
dc.subject | Learning from incidents | en_US |
dc.subject | Safety learning | en_US |
dc.subject | Safety performance | en_US |
dc.title | Improving safety performance of construction workers through learning from incidents | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 20 | - |
dc.identifier.issue | 5 | - |
dc.identifier.doi | 10.3390/ijerph20054570 | - |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | International journal of environmental research and public health, Mar. 2023, v. 20, no. 5, 4570 | - |
dcterms.isPartOf | International journal of environmental research and public health | - |
dcterms.issued | 2023-03 | - |
dc.identifier.scopus | 2-s2.0-85149937354 | - |
dc.identifier.pmid | 36901580 | - |
dc.identifier.eissn | 1660-4601 | - |
dc.identifier.artn | 4570 | - |
dc.description.validate | 202404 bcch | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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ijerph-20-04570.pdf | 1.44 MB | Adobe PDF | View/Open |
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