Prevalence of Accidents and Injuries and Related Factors of Fishermen Fishing Offshore in the North of Vietnam
Bibliographic record
Abstract
Introduction: Seafaring, particularly offshore fishing, exposes fishermen to various occupational risks leading to diseases and injuries. This study aimed to determine the prevalence of occupational risks, injury accidents, and contributing factors among offshore fishers in North Vietnam, to develop evidence-based recommendations to enhance their safety and well-being. Methods: A cross-sectional study was conducted involving 420 fishermen with a minimum of two years of experience. Interviews were conducted between 2018 and 2020. Results: The findings indicated that there is a 41.7% prevalence of accidents and an average injury rate of 280.2 per person per year. Most incidents occurred at night with 104 cases (59.4%), slips and falls 48 cases (27.4%), broken winch lines 40 cases (22.9%), and ship collisions 14 cases (8.0%) being the primary causes. The most common injuries included soft wounds in 92 cases (52.5%) and sprains/dislocations in 14 cases (8.0%). Fishermen with fewer than 10 years of experience exhibited a higher accident risk (odds ratio = 1.54; 95% confidence interval: 1.05-2.72), as did those in the role of a fisherman (odds ratio: 1.68; CI: 0.97-2.94) and those working without labor protection (odds ratio: 3.68; CI: 1.05-12.93). Conclusion: Lack of labor protection equipment increased the risk by 3.68 times, and fishermen in the friend group had a 2.02 times higher risk of injury. Addressing these risks requires adherence to labor protection regulations and safe working procedures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".