Ergonomic risks in fish processing workers in Atlantic Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it