Ergonomic risks in fish processing workers in Atlantic Canada
Bibliographic record
Abstract
Background: The aquaculture industry is growing in Canada and is particularly strong in Atlantic Canada. Workers in the fish processing industry are required to complete a variety of tasks in a typical day and there is concern for musculoskeletal disorder. Objective: The purpose of this study was to examine the daily operations of fish processing workers to determine any musculoskeletal concerns. Methods: The ergonomic assessment consisted of several plant visits to observe the processing line and the requirements of the workers. Video recordings were made of each stage of the assembly lines. The video data was analyzed to determine high-risk jobs and to identify areas of concern. Cumulative loading was assessed using posture matching software and the video data. A Job Strain Index (JSI), Rapid Upper Limb Assessment (RULA) and the revised NIOSH lifting equation were used to identify high-risk tasks. Results: The data showed that six tasks were considered high risk; sorting fish, removal of fish bones, trimming of fish, pallet loading/conveyor operation, fish processing and cleaning of the trim machine. In addition, four categories of occupational health and safety (OHS) hazard concerns were identified (physical, chemical, biological, and psychosocial). Each category was then broken into their causative agents and potential health effects on the worker. Conclusions: Several areas for improvement were identified at this seafood processing plant. Six jobs were identified as high risk and in need of intervention. Changes in pace of work, workstation height, and new equipment would also help reduce the number of musculoskeletal injuries. The issue of job rotation should also be examined to determine its impact on musculoskeletal health. Implementation of strategies to reduce musculoskeletal disorders will help to improve the health of these workers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".