Migrant Worker Health Care in Windsor-Essex County
Bibliographic record
Abstract
Migrant workers form the backbone of Canada's agricultural industry, with more than 50,000 migrant agricultural workers hired annually that largely work in the Windsor-Essex region. Largely from Mexico, the Caribbean, Guatemala, Thailand and the Philippines, these workers are usually hired through the Seasonal Agricultural Workers Program (SAWP) through temporary contracts with no direct route to permanent residency. With employment in a relatively high-risk industry and the temporary nature of their residency, treatment of workers with critical illnesses is often interrupted by the end of their contracts, with the majority unable to access the same standard of healthcare in their country of origin. Additionally, many refuse to seek initial treatment due to fear of losing employment. Our research aims to understand and prevent this outcome through a multifaceted approach. Through a mixed methods research platform, in which we perform a retrospective case analysis, interview critically ill migrant workers, and collaborate with the Mexican consulate we strive to determine the impact of this interruption on the continuity of care and discover barriers faced in accessing treatment. Through surveying healthcare professionals in the Windsor-Essex region, we aim to discover barriers faced by healthcare practitioners and potential avenues of policy change to better support their care. The ultimate objective of this project is to reform Canada's policy on the continuation of care for migrant workers. By allowing continuity of care for these critically ill migrants, Canada can fulfill an ethical obligation to support these temporary workers who play a vital component in our society.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".