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Improving Safety Performance of Construction Workers through Learning from Incidents

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Chan, Albert P. C., Guan, Junfeng, Choi, Tracy N. Y., Yang, Yang, Wu, Guangdong, Lam, Edmond·2023·International Journal of Environmental Research and Public Health
DOI 10.3390/ijerph20054570PMC PMC10002101
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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.

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  • There is a lack of comprehensive understanding regarding risk mitigation strategies implemented across different regions and limited evidence on their combined impacts on non-fatal injuries.

    “However, there is a lack of comprehensive understanding regarding the risk mitigation strategies implemented across different regions, along with limited evidence on their combined impacts on non-fatal injuries (Almaskati et al. 2024; Chan et al. 2023; Consunji et al. 2020; Van der Molen et al. 2018; Lehtola et al. 2008).”

    From Impact of risk mitigation strategies on non-fatal injuries in the construction sector in qatar: a retrospective analysis (2025) · cites this paper as Chan et al. 2023

    The Introduction (no page provided) identifies a research gap concerning learning from incidents in construction, not risk-mitigation strategies across regions. The Discussion and Section 4.2 examine joint learning-from-incidents strategies and overall safety performance, but do not address whether evidence is limited on their combined effects on non-fatal injuries. Searches for non-fatal injuries and regional strategies found no passage supporting the attributed claim. (LLM, conf=0.76)

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Authors
Chan, A. P. C., Guan, J., Choi, T. N. Y., Yang, Y., Wu, G., & Lam, E.
Affiliations
  • Hong Kong Polytechnic University (HK)
  • Chongqing University (CN)
Corresponding author
Choi, Tracy N. Y.
ORCID
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2023-03-04
Volume
20
Issue
5
Pages
4570
ISSN
1660-4601 (ISSN-L)
DOI
10.3390/ijerph20054570
PMC
PMC10002101
OpenAlex
W4323314567
Cited by
55
References
0
Citations by year
2026: 11 · 2025: 24 · 2024: 18 · 2023: 2
FWCI
14.16
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
National Natural Science Foundation of China (71971186)
Topics
  • Health Sciences › Health Professions › Radiological and Ultrasound Technology › Occupational Health and Safety Research
  • Physical Sciences › Engineering › Civil and Structural Engineering › Construction Engineering and Safety
  • Health Sciences › Health Professions › Occupational Therapy › Occupational Health and Safety Management
Keywords
learning from incidents, construction worker safety, safety performance, incident analysis, information sharing, management commitment, Bayesian networks, occupational safety, construction industry
MeSH terms
Bayes Theorem, China, Humans, Surveys and Questionnaires, Causality, Occupational Health, Safety Management, Construction Industry

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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