Occupational noise exposure and health impacts among fish harvesters: a systematic review
Bibliographic record
Abstract
BACKGROUND: Occupational noise exposure has been identified as a significant risk factor for fish harvesters. Chronic noise exposure causes hearing and other health problems and undermines the quality of life and well-being. This review paper aims to highlight noise-related auditory and non-auditory health effects among fish harvesters. MATERIALS AND METHODS: A systematic literature search approach was adopted using the following databases: PubMed, Embase, SCOPUS, Web of Science, Google Scholar, and by exploring grey literature. The literature search was conducted in 2020 (between October 15 and November 30). Relevant articles were explored by reviewing title, keywords, and abstract based on the inclusion and exclusion criteria. The full-text critical review of selected papers was made and finalized the most relevant studies. RESULTS: Initial 1,281 records were identified, exploring various databases and additional sources using relevant keywords. Duplicate articles were removed and retrieved 746 articles. After that, a screening of 746 research papers was done based on the selection criteria and finalised 28 articles for full-text review. Finally, articles were filtered based on the study's aim and extracted 17 papers for the final review. CONCLUSIONS: Noise-induced hearing loss was considered a significant health risk to fish harvesters across the studies, affecting physical and emotional well-being. The prevalence of hearing loss was observed from 6% to 80%. Other health problems, such as headache, dizziness, annoyance, stress, fatigue, elevated blood pressure, sleep disturbances, and impaired cognitive performance, were also reported. Further research is needed to validate the non-auditory health effects among fish harvesters.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".