Understanding Migrant Farmworkers’ Health and Well-Being during the Global COVID-19 Pandemic in Canada: Toward a Transnational Conceptualization of Employment Strain
Bibliographic record
Abstract
During the COVID-19 pandemic, Canada imposed certain international travel bans and work-from-home orders, yet migrant farmworkers, declared essential to national food security, were exempt from such measures. In this context, farm worksites proved to be particularly prone to COVID-19 outbreaks. To apprehend this trend, we engaged an expanded and transnational employment strain framework that identified the employment demands and resources understood from a transnational perspective, as well as the immigration, labour, and public health policies and practices contributing to and/or buffering employment demands during and after the COVID-19 pandemic. We applied mixed methods to analyze administrative data, immigration, labour, and public health policy, as well as qualitative interviews with thirty migrant farmworkers employed in Ontario and Quebec. We concluded that the deleterious outcomes of the pandemic for this group were rooted in the deplorable pre-pandemic conditions they endured. Consequently, the band-aid solutions adopted by federal and provincial governments to address these conditions before and during the pandemic were limited in their efficacy because they failed to account for the transnational employment strains among precarious status workers labouring on temporary employer-tied work permits. Such findings underscore the need for transformative policies to better support health equity among migrant farmworkers in Canada.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".