Unpacking the Mystery of the Ontario Healthcare System in Canada: Ukrainian Temporary Migrants and Healthcare System Navigation
Bibliographic record
Abstract
BackgroundIn response to the full-scale Russian invasion of Ukraine, the Government of Canada welcomed thousands of temporary migrants under the Canada-Ukraine Authorization for Emergency Travel (CUAET) program. Ukrainian temporary migrants who are settled in Ontario experience acute, chronic, and complex health issues, creating additional demand upon the healthcare system. Despite a collective awareness of difficulty in accessing existing healthcare resources, little is known about how Ukrainian temporary migrants experience and utilise the Ontario healthcare system.PurposeTo explore the lived experiences of Ukrainian temporary migrants navigating the Ontario healthcare system, and to report on the results of a knowledge translation (KTr) workshop intervention delivered with this population to assist in the development of knowledge and skills related to healthcare system navigation.MethodsA KTr workshop was delivered with ten Ukrainian temporary migrants who have temporarily settled in Toronto, Canada. Inductive and deductive thematic analysis was used.ResultsFour themes emerged: 1) concerns regarding accessibility and wait times; 2) difficulties navigating the healthcare system; 3) transnational health practices; and 4) a desire for increased involvement in the care plan.ConclusionsThis project highlights barriers to services and the need for healthcare providers to explore equitable and accessible solutions to support temporary migrants.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| 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".