A hierarchical approach to the causality of shipyard accidents with integrated machine learning methods.
Background: The number of shipyard accidents should be reduced by examining the effects of the various demographic and workplace factors on the severity of the accident. Objective: The study examines shipyard accidents and various occupational-behavioral-environmental factors affecting these acciden...
| Published in: | Work Vol. 84; no. 3; pp. 735 - 749 |
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| Main Authors: | , |
| Format: | research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
Jul2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194971350&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194971350 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: Jul2026 vid: 84 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 194971350 191675304 194971350 194971350 10.1177/10519815261422714 194971350 ppf: 735 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A hierarchical approach to the causality of shipyard accidents with integrated machine learning methods. aug: au: Yilmaz, Fatih Cal, Mural affil: Faculty of Health Sciences, Department of Occupational Health and Safety, Istanbul Yeni Yuzyil University, Istanbul, Türkiye sug: subj: Accidents, Occupational Evaluation Accidents, Occupational Prevention and Control Occupational-Related Injuries Prevention and Control Machine Learning Methods Causality Ships Human Turkiye Male Female Occupational Safety Work Environment Logistic Regression ROC Curve Descriptive Statistics Structural Equation Modeling Health Promotion Leadership Data Analysis Software Accidents, Occupational Risk Factors Risk Assessment Male Female ab: Background: The number of shipyard accidents should be reduced by examining the effects of the various demographic and workplace factors on the severity of the accident. Objective: The study examines shipyard accidents and various occupational-behavioral-environmental factors affecting these accidents to find minor accidents (or near-misses) that turned out to be major and to examine the effects of factors on the possible consequences of the accidents, to compare the predicted results with the actual results, and to investigate possible hidden reasons for the occurrence of accidents. Methods: The study uses an accident causality model and conducts experiments with a multi-factor approach on accident causality in the shipbuilding industry through logistic regression and machine learning. It performs an association rules analysis to further enhance the causality model. Results: Machine learning algorithm outputs yielded results that differed significantly from the apparent descriptive distribution of causes of major accidents. Lack of control and audit stands out as the most important accident factor in the occurrence of major accidents. Design errors and lack of training are also two important administrative factors in the occurrence of major accidents. 38.2% of major occupational accidents in shipyards are preventable or can be overcome with minor injury. In 87% of preventable major accidents, the employee had been involved in one or two previous minor incidents. Conclusion: Administrative deficiencies are prominent in major accidents. The main employer's workers and managers are at higher risk in terms of major accident exposure. The effectiveness of safety training should be increased in accordance with the changing working environment and technological conditions. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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