Primary health care utilization in the first year after arrival by refugee sponsorship model in Ontario, Canada: A population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Canada's approach to refugee resettlement includes government sponsorship, a pioneering private sponsorship model and a third blended approach. Refugees are selected and supported differently in each approach including healthcare navigation. Little is known about how well private sponsors facilitate primary care navigation and whether this changed during the large-scale 2015 Syrian resettlement initiative characterized by civic and healthcare systems engagement. METHODS AND FINDINGS: Population-based cohort study of resettled refugees arriving in Ontario between April 1, 2008 and March 31, 2017, with one-year follow-up, using linked health and demographic administrative databases. We evaluated associations of resettlement model (GARs, Privately Sponsored Refugees [PSRs], and Blended-Visa Office Referred [BVORs]) by era of arrival (pre-Syrian and Syrian era) and by country cohort, on measures of primary care (PC) navigation using adjusted Cox proportional hazards and logistic regression. There were 34,591 (pre-Syrian) and 24,757 (Syrian era) resettled refugees, approximately half of whom were GARs. Compared with the reference group pre-Syrian era PSRs, Syrian PSRs had slightly earlier PC visits (mean = 116 days [SD = 90]) (adjusted hazard ratios [aHR] = 1.19, 95% CI 1.14-1.23). Syrian GARs (mean = 72 days [SD = 65]) and BVORs (mean = 73 days [SD = 76]) had their first PC visit sooner than pre-Syrian era PSRs (mean = 149 days [SD = 86]), with respective aHRs 2.27, 95% CI 2.19-2.35 and 1.89, 95% CI 1.79-1.99. Compared to pre-Syrian PSRs, Syrian GARs and BVORs had much greater odds of a CHC visit (adjusted odds ratios 14.69, 95% CI 12.98-16.63 and 14.08, 95% 12.05-16.44 respectively) and Syrian PSRs had twice the odds of a CHC visit. CONCLUSIONS: Less timely primary care and lower odds of a CHC visit among PSRs in the first year may be attributed to selection factors and gaps in sponsors' knowledge of healthcare navigation. Improved primary care navigation outcomes in the Syrian era suggests successful health systems engagement.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".