Precarious patients: health professionals’ perspectives on providing care to Mexican and Jamaican migrants in Canada’s Seasonal Agricultural Worker Program
Bibliographic record
Abstract
INTRODUCTION: The intersecting vulnerabilities of migrant agricultural workers (MAWs) impact both their health and their access to health care in rural areas, yet rural clinicians' voices are rarely documented. The purpose of this study was to explore health professionals' perspectives on health care for MAWs in sending countries and rural Ontario, Canada. METHODS: Qualitative research design occurred over three distinct projects, using a multi-methodological approach including semi-structured interviews in Mexico, Jamaica and rural Ontario (n=43), and session field notes and questionnaires administered to healthcare providers (n=65) during knowledge exchange sessions in rural Ontario. A systematic analysis of these data was done to identify common themes, using NVivo software initially and then Microsoft Excel for application of a framework approach. RESULTS: Structural challenges posed by migrant workers' context included difficulties preventing and managing work-related conditions, employers or supervisors compromising confidentiality, and MAWs' fears of loss of employment and return to countries of origin prior to completing treatments. Structural challenges related to health services included lack of adequate translation/interpretation services and information about insurance coverage and MAWs' work and living situations; scheduling conflicts between clinic hours and MAWs' availability; and difficulties in arranging follow-up tests, treatments and examinations. Intercultural challenges included language/communication barriers; cultural barriers /perceptions; and limited professional knowledge of MAWs' migration and work contexts and MAWs' knowledge of the healthcare system. Transnational challenges arose around continuity of care, MAWs leaving Canada during/prior to receiving care, and dealing with health problems acquired in Canada. A range of responses were suggested, some in place and others requiring additional organization, testing and funding. CONCLUSION: Funding to strengthen responses to structural and intercultural challenges, including research assessing improved supports to rural health professionals serving MAWs, are needed in rural Canada and rural Mexico and Jamaica, in order to better address the structural and intersecting vulnerabilities and the care needs of this specific population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".