Hospital Care for Patients Uninsured due to Immigration Status during the COVID-19 Pandemic in Toronto: Lessons from Front-Line Knowledge Translation
Bibliographic record
Abstract
Before the COVID-19 pandemic, patients in Ontario who were uninsured due to immigration status faced barriers to hospital care that resulted in preventable illness and death. In March 2020, the Ontario Ministry of Health issued a memo indicating that it would pay for medically necessary hospital services for uninsured patients (Ontario Ministry of Health 2020). Front-line providers and research workers associated with the Health Network for Uninsured Clients (HNUC) set out to ensure that hospitals in Toronto implemented the ministry's memo. In this paper, we demonstrate a model of front-line worker-led knowledge translation informed by real-time data and anchored in clearly articulated values and goals. On April 1, 2023, the Ontario Ministry of Health cancelled this uninsured coverage (Ontario Ministry of Health 2023). Healthcare provider associations, grassroots groups and coalitions - including the HNUC - are mobilizing to see this uninsured coverage reinstated.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".