Developing a Risk Assessment Framework for Evaluating and Mitigating Occupational Exposure of Migrant Farmworkers to Enteric Pathogens in Canada’s Seasonal Agricultural Worker Program
Bibliographic record
Abstract
Seasonal migrant farmworkers are a group of workers that annually participate in the Seasonal Agricultural Worker Program (SAWP), a federally managed labour migration program set up to respond to the labour shortage in the Canadian agricultural sector. Workers spend up to eight months living and working on farms across Canada and participate in primary agricultural work opportunities that include the care of animals and the harvesting of crops. Migrant farmworkers undergo a detailed process ensuring suitability for participation in the program, which includes health screenings and medical clearances confirming workers are fit to work and are generally healthy without any signs of illness or disease. Despite the requirement for health screening prior to arrival, data describing their health after their work period has ended are scant. This work aims to evaluate the enteric disease health risks that migrant farmworkers face in the SAWP occupational setting. The agricultural setting inherently presents sources of enteric pathogens and the SAWP occupational setting increases the possibility of exposure given occupational hazards (i.e., handling or use of managed manure, the care and sanitation of animals) and elevated risk from cross-contamination and secondary transmission from one person to another given the on-farm congregate housing. \n \nRisk-based methodologies at the interface of environmental engineering and occupational and public health were used to investigate the health risks attributable to enteric pathogens that migrant farmworkers face in the SAWP occupational setting. A risk assessment involving risk identification, risk analysis, and risk evaluation and drawing on Quantitative Microbial Risk Assessment (QMRA), Hazard Analysis and Critical Control Points (HACCP) and Failure Mode and Effects Analysis (FMEA) were utilized to identify hazards and opportunities for their mitigation. The risk assessment resulted in the identification of key hazards and hazardous situations, the development of a transmission network identification of key exposure pathways, and the development of two risk matrices for the evaluation of health risks in the SAWP occupational setting. Key factors contributing to migrant farmworker health risk in the SAWP occupational setting included (1) agriculture environmental factors leading to exposure to sources of enteric pathogens in the agricultural setting, (2) infrastructure factors contributing to hazardous situations related to the migrant farmworker living and working conditions, (3) occupational factors such as the provision of health and safety training, and (4) SAWP management factors including access to health care. \n \nThe development of the risk-based framework, including the hazard identification and preliminary health risks evaluation, highlights evidence of workers experiencing relatively heightened health risk attributable to enteric pathogens as a result of the occupational setting. More broadly it also emphasizes enabled pathogen pathways and the spread of infectious disease in this occupational setting recently exemplified with multiple major outbreaks of COVID-19 among workers across distinct geographic locations. Risk tools as a part of the overall framework, including the conceptual model, transmission network, and risk matrices provide an approach to identifying, evaluating, and mitigating significant health risks posed by enteric pathogens to migrant farmworkers. Further research requiring collection and centralized reporting of migrant farmworker health data can contribute to overall framework development, helping to inform policy for the health protection of both workers and the general public.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".