Enhancing Healthcare Access for Migrant Populations Through a Student-Supported Clinic Model: A Qualitative Study
Bibliographic record
Abstract
Access to healthcare for vulnerable populations, such as Temporary Foreign Workers (TFWs), remains a critical challenge in Canada. Despite their critical contributions to the agricultural industry and North American food chain, TFWs encounter numerous barriers, such as differences in language, lack of transportation, and precarious access to healthcare due to their temporary work permit. This pressing issue prompted the emergence of Student Led Clinics (SLCs), healthcare facilities primarily managed and operated by students aiming to decrease emergency department utilization and increase healthcare accessibility for marginalized populations. However, despite the benefits, a SLC is difficult to implement into smaller communities that lack funding, resources, and neighboring professional schools. In response, this study proposes a novel healthcare delivery model, Student Supported Clinic (SSC), tailored to the unique needs of smaller communities like Windsor-Essex. Unlike traditional SLCs, the SSC model leverages a collaborative framework involving students working alongside licensed professionals while maintaining the same benefits. To develop the SSC model, semi-structured interviews will be conducted with clinic directors, facilitators, and coordinators from the twelve successful SLCs in Canada to identify key elements to implement into the SSC model. Through this research, we hope the proposed SSC framework offers a scalable and transitionary model for smaller communities. We propose this model in a manner that aligns with global sustainability goals and advocates for inclusive healthcare practices.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".