Occupational Exposure of Noise and Its Health Impacts among Fish Harvesters in Newfoundland and Labrador
Bibliographic record
Abstract
BACKGROUND AND AIM: The fishery is considered one of the most hazardous occupations. Noise exposure is a highly prevalent risk among fish harvesters causing significant occupational health issues. Occupational noise exposure can result in auditory health problems such as Noise-Induced Hearing Loss and non-auditory health problems, including annoyance, hypertension, sleep disturbance, cognitive impairment, affecting the overall quality of life. This study explores the perception of noise exposure and highlights self-reported hearing loss among fish harvesters in Newfoundland and Labrador (NL), Canada. METHODS: A cross-sectional online survey was conducted (2021) among NL fish harvesters. As the study is exploratory, the maximum number of fish harvesters working in NL was approached. The study tool was developed and validated by Suzanne C Purdy and Warwick Williams (2002). It consists of 20-item questions including perceived benefits, barriers, self-efficacy, attitude, and susceptibility towards noise and hearing loss. Additional, 16-item questions related to socio-demographic information, job profile, vessel characteristics, and self-reported hearing loss were included. RESULTS:The study is still ongoing. The preliminary results are based on the data of 78 survey participants. About 54.84 and 68% of participants agreed that work would be less stressful if it is quieter and noise has bad effects on their health other than hearing. The majority (80%) of participants felt that vessel owners are not interested in occupational health and safety. Nearly 52 and 84% disagreed that reducing noise at work and making quieter equipment is difficult. Around 55, 60% had self-reported tinnitus and hearing difficulty. CONCLUSIONS:Overall, noise is identified as a significant health risk and the leading cause of hearing loss among fish harvesters. Further quantitative research with a larger sample size and broader geographical area can help to identify other health conditions, and quantitative research can explore the existing barriers and challenges of onboard noise prevention. KEYWORDS: Occupational exposures, Occupational epidemiology
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".