Sex difference in musculoskeletal disabilities among Korean fishers: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Fishing is a physically demanding occupation with a high risk of musculoskeletal disabilities (MSDs). Although previous studies have focused on ergonomic risk factors, little attention has been paid to sex differences in the prevalence of MSDs among fishers. This study aimed to assess whether female fishers experience a higher prevalence of MSDs than male fishers and to examine whether this difference persists after adjusting for socioeconomic and occupational factors. METHODS: We analyzed cross-sectional data from 898 Korean fishers (513 men and 385 women) who participated in the 2021-2022 Fisher Health Survey. MSDs in the upper extremities, lower back, and knees were defined as scores in the top 25% of the Quick Disabilities of the Arm, Shoulder, and Hand (QuickDASH), Oswestry Disability Index, and Western Ontario and McMaster Universities Arthritis Index Short Form (WOMAC-SF), respectively. Modified Poisson regression was used to calculate the prevalence ratios (PRs) by sex, with stepwise adjustments for age, socioeconomic factors, and occupational factors. RESULTS: Female fishers had significantly higher MSD risk than male fishers across all body regions (fully adjusted PRs: upper extremity, 1.59; lower back, 1.63; knee, 1.44). Sex disparities were most pronounced among those under 60 years of age and remained significant even in older age groups. CONCLUSIONS: The elevated MSD risk among female fishers persisted despite adjusting for conventional risk factors, suggesting the influence of additional factors such as biological susceptibility, domestic labor, and gendered health reporting. Therefore, MSD prevention strategies should include sex-sensitive multidimensional approaches beyond ergonomic interventions.
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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 | 0.000 | 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 | 0.001 |
| 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".