Seafarers vs. Port State Control

Decoding Work/rest Compliance Data Disparity

Bibliographic Data

ID21520685
AuthorsBikram Singh Bhatia (0009-0009-0111-1814, World Maritime University, corresponding author), María Carrera-Arce (0000-0001-9644-0315, World Maritime University), Raphaël Baumler (0000-0003-3786-8914, World Maritime University), Michelle Grech (0000-0002-3323-3607, The University of Queensland), Michelle Rita Grech
Year2024
Volume163
Pages106105
Publication date2024-05-01
Open AccessYes
TypeARTICLE
VenueMarine Policy (JOURNAL)
Journal identifiersISSN: 0308-597X • E-ISSN: 1872-9460
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.marpol.2024.106105
OpenAlexW4394019064
LanguageEN
Citations received1
References cited24

Mandatory work/rest hours regulations guarantee a base level of fatigue management at sea while ensuring an even playing field across the industry. Although studies have highlighted concerns regarding the accuracy of compliance reporting and enforcement efficacy in detecting non-compliance, a gap remains in understanding the extent of work/rest non-compliance that may go undetected during enforcement efforts. This study compared the results of port State control (PSC) inspections conducted as part of concentrated inspection campaigns focusing on hours of work/rest regulations with the outcomes of a global seafarers’ survey. The study revealed that while compliance rates with the PSC ranged from 90.0% to 99.3%, the survey results identified seafarers reporting notably lower compliance rates with hours of work/rest regulations, ranging from 11.7% to 16.1%. Factors such as adjusting work/rest hours records, seafarers’ hesitancy to disclose non-compliance, contrasting record-keeping objectives, inadequacies in current recording systems and challenges in enforcement would explain this weighty discrepancy

Algorithm · Decoding methods · Telecommunications · Computer Science · Engineering · Maritime Navigation and Safety · Medicine · Psychology · Sleep and Work-Related Fatigue · Social Psychology · Traffic and Road Safety · Artificial Intelligence · Internal Medicine

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