Syrian Patients’ Perceptions of Care Among a Specialized Refugee Clinic and Community Clinics in Calgary, Canada
Bibliographic record
Abstract
From November 2015 to January 2017, 40,481 Syrian refugees were rapidly resettled to Canada through the “Syrian Refugee Initiative”, increasing demands on local primary healthcare clinics. We explored care perspectives among resettled Syrian refugee patients who received care at either a specialized refugee clinic or partner community clinics in Calgary, Canada. We conducted an exploratory descriptive qualitative analysis among 19 adults Syrian refugees who arrived in Calgary, Canada during the Syrian Refugee Initiative and received care either at the refugee clinic or two partner primary care clinics. We conducted 11 semi-structured interviews with individual adults or spousal pairs, to explore perceived barriers and facilitators to receiving care, and perceived opportunities for healthcare delivery improvements. All patients reported receiving high quality healthcare, facilitated communication, health navigation assistance and cultural competency as care facilitators, especially among primary care clinics. Participants also perceived integrated care between primary care providers and embedded multidisciplinary care teams within primary care clinics as a particularly important care facilitator. Major perceived care barriers, particularly among specialty care included: communication difficulties, long wait-times and inadequate health insurance coverage. Perceived opportunities for improvement included adequate insurance coverage for dental care and prescription medications, as well as improved health navigation and promotion services. Recently resettled Syrian refugee patients identified integrated, team-based and culturally competent primary healthcare, with translation and health navigation services as optimal care factors. However, perceived challenges accessing and receiving specialty care and dental care as well as insufficient prescription medication insurance coverage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".