Mexican migrant agricultural workers’ experiences of the public health measures during the COVID-19 pandemic in the Okanagan
Bibliographic record
Abstract
Migrant agricultural workers (MAWs) play a key role in ensuring Canadian food security, with nearly half of these workers coming from Mexico. Extensive research reveals that MAWs in Canada face numerous barriers to access healthcare and face a high risk of health inequities, including substandard housing and hazardous working conditions. The COVID-19 pandemic exacerbated many of these health inequities, leading to some of the largest COVID-19 outbreaks outside long-term care facilities in Canada, along with the reported deaths of nine MAWs. Despite these critical issues, there is limited research exploring Mexican MAWs' barriers, facilitators and adverse effects associated with the pandemic response and its public health measures (PHM). To address this gap, this study focused on Mexican MAWs in the Okanagan region. The study employs community-based research using an interpretive description methodology and is guided by an intersectionality lens. The overarching question is as follows: What are the experiences of Mexican MAWs with PHM during the COVID-19 pandemic in the Okanagan region? Data were collected through field notes taken during volunteer work and individual semi-structured interviews with 14 Mexican MAWs, two healthcare professionals, and three support individuals. Interview transcripts and field notes were coded and subjected to thematic analysis using the NVivo software. Results of this study describe how pandemic responses failed to protect MAWs and to address the precarious conditions of Mexican MAWs, exacerbating avoidable inequities. The profound influence of deeply rooted structural factors that underpinned and perpetuated the health inequities experienced by Mexican MAWs throughout the pandemic are detailed as well as the invaluable role of community-based organizations in serving as crucial support for Mexican MAWs, and their significance as indispensable partners and pivotal allies to health authorities during times of crisis. These findings contribute to advancing knowledge and informing policy recommendations to address health inequities faced by Mexican MAWs, particularly in pandemic contexts, and add to growing calls for comprehensive policy reform of the Seasonal Workers Agricultural Program (SWAP).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".