Impact of COVID-19 on an Urban Refugee Population
Bibliographic record
Abstract
Purpose: The COVID-19 pandemic has brought to light many systemic inequities in health care delivery. As medical communities work to address the disproportionate effects of COVID-19 on vulnerable populations, it is crucial to include refugees in the public health response. Language barriers, poor health literacy, and low socioeconomic status render refugee populations highly susceptible to negative outcomes from the COVID-19 pandemic. To better understand the refugee experience with COVID-19, we constructed and administered a survey among refugee populations in Houston, Texas. Methods: Our 49-question cross-sectional survey was administered to 44 participants in Arabic, Burmese, Dari, English, Kiswahili, Nepali, Spanish, or Urdu with the use of refugee resettlement case managers acting as translators. The survey encompassed three domains, including a general knowledge assessment of COVID-19, subjective experiences with COVID-19, and risk communication practices within refugee populations. Results: The majority of refugees surveyed admitted to worrying about the effects of COVID-19 on their community (88.6%). The negative consequences of the COVID-19 pandemic included financial adversity (65.1%) and significant disruption of children's education (62.8%). Although 50.0% of participants self-reported proficiency in English, translation services were used with 75.0% of participants to ensure full comprehension. Conclusions: The implications of our findings suggest that local refugee populations require heightened support during the COVID-19 pandemic. Tailored interventions should encompass comprehensive translation and interpretation services, financial assistance, and academic interventions for refugee youth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".