Evidence map›Paper›PMID 41972795›Full record

ArticleInquiry : a journal of medical care organization, provision and financing

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

Yongwei Jiang, Zhendong Tang, Hua Liu, Wenjie Cao, Zhiwei Zhao, Özkan Uğurlu, Xinjian Wang

Abstract read
In one paragraph

Article in Inquiry : a journal of medical care organization, provision and financing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yongwei JiangDalian Maritime University, China.
Zhendong TangDalian Maritime University, China.
Hua LiuDalian Maritime University, China.
Wenjie CaoDalian Maritime University, China.ORCID 0009-0007-1224-3367
Zhiwei ZhaoDalian Maritime University, China.ORCID 0000-0002-6983-7381
Özkan UğurluOrdu University, Turkey.ORCID 0000-0002-3788-1759
Xinjian WangDalian Maritime University, China.ORCID 0000-0002-7469-6237

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Machine LearningMental HealthShipsBayes TheoremClassification AlgorithmsHumansPrediction AlgorithmsPredictive Learning ModelsRandom ForestRisk FactorsWorking Conditionsmachine learningrandom forestseafarer mental healthSHapley Additive exPlanations (SHAP)stressorstransportation safetywork-life conditions

Identifiers

PMID41972795
PMCPMC13080192

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.