“We Should Not Call an Ambulance, Even If We are Very Sick”: Ukrainian Refugee Women’s Experiences in the United States Healthcare System
Bibliographic record
Abstract
As the number and diversity of refugees worldwide increases, healthcare providers working with these populations face unique challenges. Thus, healthcare providers in host countries have limited understanding of challenges refugees face when using healthcare in host countries. The goal of the study is to achieve an interpretive understanding of women refugees' perspectives on their interactions with healthcare systems and providers, as well as to identify factors and processes that women refugees view as enabling or hindering their access to healthcare. Study participants were refugee women from Ukraine in the US (N = 17), who volunteered to take part in semi-structured interviews about their experiences in the US healthcare. The interviews were transcribed in original languages and analyzed using Charmaz's constructivist grounded theory methodology, which is based on constant comparison between codes and data exemplars within and between interviews. Three priority theoretical elements were identified: (1) barriers to care, which encompassed codes: uncertainty about costs, lack of health insurance, time constraints, difficulties in communication, problems of distance and transportation, and finding a trustworthy provider; (2) systemic and organizational features that hinder care, which encompassed codes: the system is confusing, inconsistencies across organizations and providers; limited scope of organizations meant to help refugees; and (3) processes and factors that do or would alleviate impediments to care, which encompassed codes: clear and relevant information, getting help from others, addressing patients' concerns, acknowledging patient circumstances. The study emphasizes the importance of continuity of care in refugee health, indicates the culture-bound nature of trust in healthcare providers, and underscores the essential role of non-formal and non-structured support for refugees.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".