Bibliographic record
Abstract
During the COVID-19 pandemic, refugee women in the United States faced significant challenges to sustain their livelihoods, such as losing jobs and health care, becoming essential workers, and finding oneself again in unprecedented situations of limited mobility. These impacts reflect dynamics in migrant health literacy including language proficiency (skills-based approaches) as well as experiences, identities, and power relations in society (socio-cultural approaches). In this article, I explore these dynamics through a gender perspective with a focus on intra-familial health brokering, empowerment-based health education, and health information mapping by drawing on ethnographic research from Portland, Oregon. This includes interviews with 15 refugee women and representatives of organizations working in the context of migration as well as observations of service-providing community efforts. My interviews and observations demonstrate that disruptions in language learning, socio-cultural barriers, and limited access to health-related information resources have posed significant challenges to refugee women’s livelihoods during the pandemic. I suggest that English as a Second Language (ESL) classes can be imperative in addressing these challenges as the classes provide a space for language learning, intercultural dialogue, and information sharing in gender-responsive ways.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".