Incidence of self-reported occupational injuries in seafaring--an international study
Bibliographic record
Abstract
AIMS: Seafaring is known as a high-risk industry. The aims were to describe the incidence of non-fatal injuries among seafarers, including testing the hypothesis that long working hours might result in higher injury rates. METHODS: A questionnaire study of injury on the latest tour of duty was carried out among seafarers in 11 countries with 6461 participants. The seafarers were asked if they were injured during the latest tour of duty and what was the number of hours worked. RESULTS: During the latest tour of duty, 9.1% of all seafarers were injured and 4.3% had an injury with at least 1 day of incapacity. The injury incidence rates for cargo ships and tankers: 39.5 per 1 million work hours and 37.6 per 100,000 days. Multivariate analyses: incidence rate ratios (IRR) for >70 working hours per week compared with <57 h: 0.90 [95% confidence interval (95% CI) = 0.61-1.32]; non-officers compared with officers: IRR = 1.57 (95% CI = 1.14-2.15); seafarers <35 years compared with > or =35 years of age: IRR = 2.11 (1.57-2.86); tour lengths > or =117 days compared with <117 days: IRR = 0.27 (0.19-0.39). Main work area on the deck and in the service area compared with work in the engine room: IRR = 0.37 (0.27-0.52) and IRR = 0.49 (0.26-0.91), respectively. CONCLUSIONS: There was no evidence that long working hours alone resulted in higher injury rates. Low self-perceived health, lack of use of personal protection and lack of occupational safety on board were significantly related to an increase in the injury risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".