Caring for refugees and newcomers in the post–COVID-19 era
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it