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The subjective measurement of seafarers’ fatigue levels and mental symptoms

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  • Elİf Bal BeşİkÇİ
  • Leyla Tavacıoğlu
  • özcan Arslan

Abstract

Human error is the most important factor causing many ship accidents in maritime industry despite advanced technology and international regulations. Fatigue in seafarers is a well-known problem and a serious cause of ship accidents. There are many factors unique to the marine environment raising the potential for fatigue at sea. Due to the difficulties in measuring human fatigue and also in suggesting fatigue to be a root cause of accident, it is important to devise methods to detect and quantify the fatigue and mental symptoms. In this study, ‘Piper Fatigue Scale’ (PFS) has been used for measuring fatigue level and ‘Symptom Checklist 90- Revised’ (SCL-90-R) for detecting the severity of mental symptoms. Data analyses were performed using the SPSS (Statistical Package for the Social Sciences) software. According to the results of PFS analysis, a slight degree of fatigue is detected in all sub-dimensions of the scale. According to the results of SCL-90-R analysis, the distress of mental symptoms perceived by seafarers is not generally highly detected. In conclusion, the purpose of this study is to determine, by using subjective measurements, the fatigue level and mental symptoms among seafarers caused by working conditions on-board.

Suggested Citation

  • Elİf Bal BeşİkÇİ & Leyla Tavacıoğlu & özcan Arslan, 2016. "The subjective measurement of seafarers’ fatigue levels and mental symptoms," Maritime Policy & Management, Taylor & Francis Journals, vol. 43(3), pages 329-343, April.
    • Handle: RePEc:taf:marpmg:v:43:y:2016:i:3:p:329-343
    DOI: 10.1080/03088839.2015.1047426
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    Citations

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    Cited by:

    1. Ji An & Yun Liu & Yujie Sun & Chen Liu, 2020. "Impact of Work–Family Conflict, Job Stress and Job Satisfaction on Seafarer Performance," IJERPH, MDPI, vol. 17(7), pages 1-14, March.
    2. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Fan, Shiqi & Yang, Zaili, 2023. "Towards objective human performance measurement for maritime safety: A new psychophysiological data-driven machine learning method," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    4. Li, Xue & Zhou, Yusheng & Yuen, Kum Fai, 2022. "A systematic review on seafarer health: Conditions, antecedents and interventions," Transport Policy, Elsevier, vol. 122(C), pages 11-25.
    5. Yuan Gu & Dongbei Liu & Guoping Zheng & Chuanyong Yang & Zhen Dong & Eugene Y. J. Tee, 2020. "The Effects of Chinese Seafarers’ Job Demands on Turnover Intention: The Role of Fun at Work," IJERPH, MDPI, vol. 17(14), pages 1-14, July.
    6. Fan, Shiqi & Yang, Zaili, 2024. "Accident data-driven human fatigue analysis in maritime transport using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Heij, C. & Knapp, S., 2018. "Predictive power of inspection outcomes for future shipping accidents," Econometric Institute Research Papers EI2018-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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