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Record W4417104136 · doi:10.1093/eurpub/ckaf180.130

488 Characterizing Canada’s refugee healthcare system: insights from a national atlas

2025· article· en· W4417104136 on OpenAlexaffabout
Gabriel E. Fabreau, Nesma El- Shazly, Mohammad Yasir Essar, Eric Norrie, Vaughan Love, C. Newton Price, Annalee Coakley

Bibliographic record

VenueEuropean Journal of Public Health · 2025
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsRefugeeHealth careWorkforceGeospatial analysisPopulationMultidisciplinary approachImmigrationCitizenshipService delivery framework

Abstract

fetched live from OpenAlex

Abstract OP 17: Refugees and Asylum Seekers 1, B210 (FCSH), September 4, 2025, 13:30 - 14:30 Aims Despite national clinical guidelines, refugee healthcare delivery in Canada remains fragmented. This study systematically characterized healthcare services for refugees and asylum claimants across Canada using a national survey of healthcare organizations, integrated with federal immigration data, to develop an interactive online Atlas. Methods We conducted a national cross-sectional survey of 191 healthcare and settlement organizations across all provinces and territories. The survey captured clinic structures, service delivery models, workforce composition, financing mechanisms, and service challenges. These data were integrated with publicly available datasets from Immigration, Refugees and Citizenship Canada (IRCC) and Statistics Canada (2015–2024). The WHO Health Systems Building Blocks framework guided the analysis. Findings were aggregated at national, provincial, and municipal levels, normalizing service capacity by population size. The Atlas, built using PowerBI, provides geospatial mapping and jurisdictional comparisons. Results The Atlas reveals significant variation in refugee healthcare services across Canada. Key strengths include robust health information systems, but gaps persist in service delivery and access to essential medicines. While 92% of clinics provide post-arrival health screening, only 67% offer structured chronic disease management, and 58% provide dedicated mental health services. Interpreter services remain inconsistent, with 42% of clinics reporting limited availability. Provincial analyses highlight workforce and funding disparities, while municipal data expose coordination challenges. Geographic mismatches between clinic locations and refugee settlement patterns indicate systemic misalignments. The Atlas also identifies promising care models, including multidisciplinary refugee health clinics and community-based integration programs. Conclusions The Canadian Refugee Healthcare System Atlas is a critical tool for policymakers, healthcare providers, and researchers, offering comprehensive data to improve refugee health services and coordination. By identifying system strengths and gaps, it facilitates evidence-based decision-making and resource allocation. Future efforts should focus on updating clinical guidelines, enhancing cross-sector collaboration, and integrating digital health innovations to strengthen refugee healthcare nationwide.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.646

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0100.029
Science and technology studies0.0050.001
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.054
GPT teacher head0.324
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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