Impact of COVID-19 on an Urban Refugee Population
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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