Refugee healthcare resilience and burdens: a 10-year mixed-methods analysis of system shocks in Canada
Bibliographic record
Abstract
Background: System shocks, including policy changes, refugee surges, and pandemics, strain healthcare systems. These shocks compound refugee health vulnerabilities, limiting care but may also trigger adaptive responses that build resilience. Understanding local responses is essential for resilient care models. Methods: This sequential explanatory mixed-methods retrospective study (2011-2020) examined how a refugee health centre in Alberta, Canada, responded to four shocks: Interim Federal Health Program (IFHP) Cuts (2012), Syrian Surge (2015), Yazidi Resettlement (2017), and COVID-19 (2020). We hypothesized that each shock would drive temporary utilization changes, reflected in level (immediate) and slope (monthly rate of change) shifts, with corresponding impacts on patients and providers. Interrupted time series analysis estimated level and slope differences in monthly appointments (total, family physician, specialist, multidisciplinary team). Interviews with health centre leaders were thematically analyzed using an adapted Health System Resilience framework and integrated with quantitative findings to assess resilience and operational burdens. Findings: Between 2011 and 2020, 10,661 refugees from 106 countries attended 107,642 appointments. The mean age was 24.49 years (SD 17.09; range 0-107), and 47.82% (5094) were female. Mean monthly appointments increased from 455 to 2,208, with an adjusted level difference of +1656.06 and slope increase of +95.68 (p < 0.0.01). Family physician and multidisciplinary team appointments rose substantially, while specialist care declined during COVID-19. Mean provider hours increased from 175.8 to 1097.3 (6.2-fold). Qualitative analysis indicated resilience capacities but also burnout, vicarious trauma, and financial strain. Integration revealed the centre developed resilience but experienced operational burden. Interpretation: Over a decade, the centre adapted to successive shocks, transforming into a beacon clinic. It demonstrated resilience through care expansion and innovation, at the cost of operational burden. Funding: O'Brien Institute for Public Health and the MSI Foundation of Alberta.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".