Exploring The Experience Of Heart Disease Among Burmese Immigrants In The United States
Bibliographic record
Abstract
The ongoing political and military turmoil in Myanmar has resulted in a significant increase in the migration of Burmese individuals to the United States in recent years. This demographic shift has brought attention to the health challenges faced by Burmese immigrants, particularly with regard to heart disease (heart attack). The complexities of the U.S. healthcare system, compounded by legal and social barriers, place immigrants at greater risk for health disparities. This study examines the health challenges of Burmese immigrants in Tulsa, Oklahoma, with a specific focus on their perceptions and experiences related to heart disease, as explored through illness narratives. Utilizing qualitative research methods, including participant observation and semi-structured interviews based on the McGill Illness Narrative Interview (MINI), this research investigates how Burmese immigrants understand and manage heart disease. Seven participants, being diagnosed with heart attack or experienced heart attack, provided in-depth illness narratives, offering insights into the manifestation of the condition, treatment choices, and lifestyle adjustments in the context of migration. Burmese immigrants experience structural trauma and vulnerability due to past violence, forced migration, and ongoing social and economic challenges, leading to higher risks of health issues like heart disease such as heart attack. The findings included that to improve health outcomes, it is essential to provide culturally appropriate health education, enhance healthcare access, and address systemic barriers such as language and economic instability.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".