Implementation of a Medical Education Service Learning Program Centred on International Agricultural Workers (IAWs) in Southwestern Ontario
Bibliographic record
Abstract
Background International Agricultural Workers (IAWs) are vital to Canadian agriculture but face significant language and cultural barriers. Windsor-Essex is uniquely situated to support programs serving IAWs, as over 50% of Canada's IAW population resides in this region. An English language support program was established with the University of Windsor (UoW) and Schulich School of Medicine (SsoM) to address barriers exacerbating healthcare challenges. Objectives This work seeks to report on the program's implementation and protocols and assess effectiveness in enhancing language proficiency, community integration, and medical student engagement with IAWs. Proposed Methods IAWs were recruited from the South Essex Community Council (SECC), and English partners from UoW and SSoM. Following an oral presentation outlining the program, 18 IAWs and 15 medical students enrolled. Participants were paired based on availability and language proficiency. Partners were given a weekly slide deck that guided conversations aligned with IAWs' language lessons. Weekly reflections, monthly meetings, surveys and interviews will be conducted with medical student volunteers to evaluate their experiences and program impact. Future Directions Preliminary survey and interview results indicate positive outcomes for both IAWs and participating medical students. IAWs reported improved English proficiency and a greater sense of community inclusion. Medical students demonstrated increased cultural awareness and understanding of challenges faced by IAWs, fostering a deeper commitment to addressing inequities in healthcare. This work underscores the potential of a language support program to enhance cultural competency among future healthcare professionals while improving quality of life and access to resources for IAWs.
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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 | 0.010 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".