Seasonal Migrant Workers Perceived Working Conditions and Speculative Opinions on Possible Uptake of Exoskeleton with Respect to Tasks and Environment: A Case Study in Plant Nursery
Bibliographic record
Abstract
Seasonal migrant farmworkers are essential to the success of agriculture in Quebec as they provide the labor needed to produce crops and animals. Notwithstanding, these workers are often at risk of occupational health and safety hazards, while only a few interventions have been implemented to improve the situation. Modern engineering interventions like exoskeleton devices have been introduced to reduce the risk of developing musculoskeletal disorders in other industries, but nothing much has been done in agriculture. This paper employed a mixed-method approach to evaluate the effect of environmental conditions and physical activities on farmworkers’ bodies and sensations and explore their speculative opinions about exoskeletons for their tasks. This study took place in a large plant nursery. Data were collected through field observations, written questionnaires, and semi-structured interviews. The analysis showed heat, humidity, cold, and rain affect farmworkers in feeling sore, worn out, tired, weak, and suffocated. The arms and the back were the body parts most affected by the repetitive bending over and carrying the load. Farmworkers’ exoskeleton perceptions were positive, remarking benefits such as making the task easier, improving posture, reducing fatigue, and protecting the body. The barriers that emerged were concerning the exoskeleton weight, being uncomfortable to wear, causing heat, restricting mobility, not allowing flexibility to change tasks, and not allowing space to work in tight workplaces. The study includes strategies to ensure credibility, reliability, and transferability. Future investigations could test exoskeletons on farmworkers and conduct the cost benefits of exoskeletons in agriculture.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".