Caring for refugees and newcomers in the post–COVID-19 era
Bibliographic record
Abstract
OBJECTIVE: To guide clinicians working in a range of primary care clinical settings on how to provide effective care and support for refugees and newcomers during and after the coronavirus disease 2019 (COVID-19) pandemic. SOURCES OF INFORMATION: The described approach integrates recommendations from evidence-based clinical guidelines on refugee health and COVID-19, practical lessons learned from Canadian Refugee Health Network clinicians working in a variety of primary care settings, and contributions from persons with lived experience of forced migration. MAIN MESSAGE: The COVID-19 pandemic has amplified health and social inequities for refugees, asylum seekers, undocumented migrants, transient migrant workers, and other newcomers. Refugees and newcomers face front-line exposure risks, difficulties accessing COVID-19 testing, exacerbation of mental health concerns, and challenges accessing health care, social, and settlement supports. Existing guidelines for clinical care of refugees are useful, but creative case-by-case strategies must be employed to overcome additional barriers in the context of COVID-19 and new care environments, such as the need for virtual interpretation and digital literacy skills. Clinicians can address inequities and advocate for improved services in collaboration with community partners. CONCLUSION: The COVID-19 pandemic is amplifying structural inequities. Refugees and newcomers require and deserve effective health care and support during this challenging time. This article outlines practical approaches and advocacy priorities for providing care in the COVID-19 context.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".