Work-Life Conditions as the Primary Determinant of Seafarer Mental Health: An Explainable Machine Learning Analysis.
Seafarer mental health has become an increasingly critical concern due to its substantial implications for transportation safety and operational performance. Traditional analytical approaches often inadequately capture the complex, non-linear interactions among the multidimensional determinants invo...
| Publicado en: | Inquiry (00469580) Vol. 63; pp. 1 - 16 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
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Sage Publications Inc.
4/13/2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=192982120&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 192982120 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 4/13/2026 vid: 63 pid: 344 pub: Sage Publications Inc. artinfo: ui: 192982120 10.1177/00469580261438708 ppf: 1 ppct: 15 formats: tig: atl: Work-Life Conditions as the Primary Determinant of Seafarer Mental Health: An Explainable Machine Learning Analysis. aug: au: Jiang, Yongwei Tang, Zhendong Liu, Hua Cao, Wenjie Zhao, Zhiwei Uğurlu, Özkan Wang, Xinjian affil: Dalian Maritime University, China Liverpool John Moores University, UK Ordu University, Turkey su: Mental illness risk factors Employee psychology Competency assessment (Law) Quality of work life Random forest algorithms Ships Prediction models Work environment Questionnaires Research evaluation Statistical reliability Psychological stress Machine learning Accuracy Industrial safety Evaluation sug: subj: Mental illness risk factors Employee psychology Competency assessment (Law) Quality of work life Random forest algorithms Ships Prediction models Work environment Questionnaires Research evaluation Statistical reliability Psychological stress Machine learning Accuracy Industrial safety Evaluation keyword: machine learning random forest seafarer mental health SHapley Additive exPlanations (SHAP) stressors transportation safety work-life conditions ab: Seafarer mental health has become an increasingly critical concern due to its substantial implications for transportation safety and operational performance. Traditional analytical approaches often inadequately capture the complex, non-linear interactions among the multidimensional determinants involved. To bridge this gap, this study proposes an explainable machine learning (ML) framework that integrates a Random Forest classifier with SHapley Additive exPlanations (SHAP) for simultaneous prediction and interpretation. Based on a survey of 500 seafarers, 12 risk factors were preprocessed through label encoding and the dataset was split into training and test sets using stratified sampling. A Random Forest model, optimized via Bayesian hyperparameter tuning, was employed to predict psychological states, with performance evaluated through accuracy, precision, recall, and F1-score. SHAP analysis was then applied to quantify global feature importance and to examine individual prediction mechanisms and interaction effects. The results identify current work-life conditions as the most influential determinant, exhibiting a polarizing effect on psychological states that substantially outweighs other factors. Furthermore, favorable environmental conditions amplify the positive effects of career development and social recognition, whereas high onboard service pressure persistently undermines mental health even under otherwise optimal circumstances. These findings underscore the necessity of prioritizing systemic environmental improvements as a foundation for effective psychological interventions, suggesting that tailored support strategies should be implemented subsequent to such enhancements. This study provides a data-driven, interpretable framework to support precision mental health management in maritime operations. These findings advocate for maritime policymakers and shipping companies to prioritize systemic improvements in onboard living and working conditions as a foundational strategy, complemented by targeted psychological support and enhanced awareness of mental health services. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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