568 A Collaborative, Community-Based Health Centre to Address Refugee Health in Canada: The New Canadians Health Centre
Bibliographic record
Abstract
Abstract EP1.5, e-Poster Terminal 1, September 5, 2025, 13:05 - 13:40 Aims Refugees often have untreated health conditions that require timely care. In Canada, settlement agencies support government-assisted refugees during their first year; however, connecting them with needed healthcare can be difficult due to language barriers, a lack of culturally appropriate care, and a physician shortage. In response, specialized refugee health centres have increasingly emerged, including the New Canadians Health Centre (NCHC) in western Canada, which was established through a collaborative partnership with the local settlement agency. This presentation explores the establishment and role of such collaborative models in supporting refugee health. Methods To evaluate the NCHC’s care model, a research partnership was formed with the Evaluation Capacity Network at the University of Alberta. Using a community-based participatory research approach, a document review and interviews with key informants involved in the NCHC’s early development were conducted. These data are supplemented by anecdotal insights from frontline staff to understand how the partnership was formed and how care is delivered. Results This presentation will describe the collaborative partnership between the NCHC and a local resettlement agency in Edmonton to address refugees’ health needs. Frontline staff will share insights on: (1) local refugee health needs; (2) the process of building a settlement agency and health centre partnership; (3) the NCHC’s approach and model for addressing refugee health; and (4) emerging learnings from this partnership. Conclusions This presentation will highlight practical insights into the development of community-driven partnerships between settlement and healthcare organizations and offer lessons for designing responsive models to better support refugee health globally.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.001 |
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".