Refugee Healthcare Resilience and Burdens: A 10-year Mixed-Methods Analysis of System Shocks in Canada
Bibliographic record
Abstract
Abstract Background System shocks, including sudden policy changes, refugee surges and pandemics, strain healthcare systems. These shocks compound existing vulnerabilities in refugee healthcare, limiting ability to provide patient care, but can also catalyze resilient adaptations. Investigating how local refugee health systems respond to shocks is critical to understanding resilience. Methods We conducted a sequential explanatory mixed-methods study (2011–2020) at a specialized refugee health centre in Alberta, Canada, investigating four health system shocks: IFHP Funding Cuts (2012), Syrian Surge (2015), Yazidi Resettlement (2017), and COVID-19 (2020). We analyzed patient sociodemographic characteristics, health center utilization, and healthcare provider supply, conducting interrupted time series analysis of mean monthly appointments (total, family physicians, specialists and multidisciplinary team) and rates of change. We adapted a Health System Resilience framework to thematically analyze interviews with centre leaders and integrated these findings with quantitative findings to assess resilience and operational burdens. Findings From 2011 to 2020, 10,661 refugees from 106 countries attended 107,642 appointments. Mean monthly appointments rose from 455 to 2,208 (3.9-fold, p<0.01). Monthly appointments increased between IFHP and Syrian periods (610.8 to 937.9, p<0.01), but not between Syrian Surge and Yazidi Resettlement (p=0.29). During COVID-19, mean appointments remained stable (1,412.4 to 1,414.0, p=0.11), but additional monthly appointments rose from 6.3 to 110.4 (17.5-fold, p<0.01). Over ten years, mean provider hours increased from 320 to 736 (2.3-fold), and from 59.5 to 871.4 (14.6-fold) for family physicians and multidisciplinary team members. Qualitative analysis revealed resilience capacities but highlighted costs such as burnout, vicarious trauma, and financial strain. Integration showed the centre developed resilience but experienced notable operational burden. Interpretation Over a decade, a specialized refugee health centre adapted to successive shocks, transforming into a beacon clinic. It demonstrated resilience through care expansion and innovation, but with notable costs, financially and to health worker wellbeing. Funding None
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".