Family Medicine With Refugee Newcomers During the COVID-19 Pandemic
Bibliographic record
Abstract
Certain members of society are disproportionately affected by the COVID-19 crisis and the added strain being placed on already overextended health care systems. In this article, we focus on refugee newcomers. We outline vulnerabilities refugee newcomers face in the context of COVID-19, including barriers to accessing health care services, disproportionate rates of mental health concerns, financial constraints, racism, and higher likelihoods of living in relatively higher density and multigenerational dwellings. In addition, we describe the response to COVID-19 by a community-based refugee primary health center in Ontario, Canada. This includes how the clinic has initially responded to the crisis as well as recommendations for providing services to refugee newcomers as the COVID-19 crisis evolves. Recommendations include the following actions: (1) consider social determinants of health in the new context of COVID-19; (2) provide services through a trauma-informed lens; (3) increase focus on continuity of health and mental health care; (4) mobilize International Medical Graduates for triaging patients based on COVID-19 symptoms; and (5) diversify communication efforts to educate refugees about COVID-19.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".