Fatigue as a latent risk factor in maritime safety systems: A systematic review and implications for reliability analysis
Bibliographic record
Abstract
• A PRISMA-based review found only 5.6 % of reports cited fatigue as a causal factor. • Fatigue is underreported in marine accident data despite regulatory attention. • Investigation reports often lack rest logs, ergonomic data, or FRMS evidence. • Fatigue risks stem from systemic conditions, not isolated individual failures. Fatigue is a well-recognised contributor to human error in maritime operations, yet its presence is consistently underreported in official accident investigations. This omission is more than a statistical shortfall; it constitutes a latent hazard within the investigative process, distorting human reliability models and weakening maritime risk assessments. Despite guidance from the International Maritime Organization, many investigations fail to recognise or substantiate fatigue, undermining the accuracy of causal analysis. This study systematically reviewed 1011 marine casualty reports published between 2017 and 2025 by the European Maritime Casualty Information Platform, the United States National Transportation Safety Board, the Australian Transport Safety Bureau and the Transportation Safety Board of Canada. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the review evaluated whether fatigue was identified, documented, and supported with empirical evidence. Fatigue was cited in only 29 cases (5.6 %), well below prevalence rates reported in prior research. Common investigative gaps included incomplete rest-hour records, lack of ergonomic and environmental data, and absence of Fatigue Risk Management System integration. By evidencing fatigue underreporting as a systemic blind spot, this study underscores the urgent need for standardised fatigue metrics, mandatory investigative protocols, and alignment of Safety Management Systems with empirical human factors evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.017 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".