Start of the Season in a Seasonal Work Context: A Better Understanding of the Difficulties Experienced by Seasonal Workers in the Food Processing Industry for the Prevention of Work-Related Musculoskeletal Disorders
Bibliographic record
Abstract
The specific period of the start of a new working season and a return to work after the off-season seems to be a critical moment for the musculoskeletal health of seasonal workers. This study aims to identify the difficulties and working conditions encountered by seasonal workers in this particular period of the working season which may increase the risk of work-related musculoskeletal disorders (WMSDs). An in-depth ergonomic work activity study, combined with a multiple case study of eight seasonal workers from a meat processing facility, was conducted. Various interviews (n = 24) and observations of work activity, organization, and production (n = 96 h) were held at different moments (off-season, return to work at the start of the season, and during the season). Critical work situations exposing workers to WMSD risks emerged and highlighted a diversity of difficulties, such as accomplishing work activity involving strong physical strain and a significant and underestimated mental load, and having to rapidly develop new skills or re-learn working strategies after a long off-period. The study findings have implications for developing actions to prevent WMSDs that target working conditions and support a return to work for returning seasonal workers and a start of work for new seasonal workers, and to address work disability in this context.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".