Work-Life Conditions as the Primary Determinant of Seafarer Mental Health: An Explainable Machine Learning Analysis.

IF 3 4区 医学 Q3 HEALTH CARE SCIENCES & SERVICES
Yongwei Jiang, Zhendong Tang, Hua Liu, Wenjie Cao, Zhiwei Zhao, Özkan Uğurlu, Xinjian Wang
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引用次数: 0

Abstract

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.

工作-生活条件是海员心理健康的主要决定因素:一种可解释的机器学习分析。
由于海员心理健康对运输安全和业务绩效产生重大影响,它已成为一个日益重要的问题。传统的分析方法往往不能充分捕捉所涉及的多维决定因素之间复杂的非线性相互作用。为了弥补这一差距,本研究提出了一个可解释的机器学习(ML)框架,该框架将随机森林分类器与SHapley加性解释(SHAP)集成在一起,用于同时预测和解释。基于对500名海员的调查,通过标签编码对12个危险因素进行预处理,并使用分层抽样将数据集分为训练集和测试集。采用贝叶斯超参数优化的随机森林模型来预测心理状态,并通过准确性、精密度、召回率和f1得分来评估其性能。然后应用SHAP分析来量化全局特征的重要性,并检查个体预测机制和相互作用的影响。研究结果表明,当前的工作生活状况是最具影响力的决定因素,对心理状态的两极分化影响远远超过其他因素。此外,良好的环境条件放大了职业发展和社会认可的积极作用,而即使在其他最佳环境下,高船上服务压力也会持续损害心理健康。这些发现强调了将系统环境改善作为有效心理干预基础的必要性,并建议在这种改善之后实施量身定制的支持策略。本研究提供了一个数据驱动的、可解释的框架,以支持海上行动中的精确心理健康管理。这些发现倡导海事政策制定者和航运公司将系统改善船上生活和工作条件作为一项基本战略,并辅以有针对性的心理支持和提高心理健康服务意识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
192
审稿时长
>12 weeks
期刊介绍: INQUIRY is a peer-reviewed open access journal whose msision is to to improve health by sharing research spanning health care, including public health, health services, and health policy.
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