A review of fatal accident incidence rate trends in fishing
Bibliographic record
Abstract
BACKGROUND: Injury prevention in fishing is one of the most important occupational health challenges. AIM: The aim was to describe and compare internationally the trends of the fatal injury incidence rates and to discuss the impact of the implemented safety programs. MATERIALS AND METHODS: The review is based on journal articles and reports from the maritime authorities in Poland, United Kingdom, Norway, Iceland, Denmark, United States and Alaska and Canada. The original incidence rates were recalculated as per 1,000 person-years for international comparison of the trends. RESULTS: The risk of fatal accidents in fishing in the northern countries has been reduced by around 50% to an average of about 1 per 1,000 person-years. Norway and Canada keep the lowest rates with around 0.5 and 0.25 per 1,000 person-years. About half of the fatal injuries are related to vessel disasters and drowning. The safety programs seem to have good effects, but the risk is still about 25 to 50 times higher than for onshore workers. CONCLUSIONS: The overall fatal injury rates in the European and North American studies decreased by around 50% most probably as result of the implemented safety programs. However the high risk in fishing compared to onshore workers calls for continued and intensified safety programs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".