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.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What Socratic holds
Registered trials
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.