Healthcare Access and Barriers Among Older Asian Americans: Cultural Influences and Systemic Challenges
Bibliographic record
Abstract
Abstract Although nearly a quarter of the 19 million Asian immigrants in the U.S. call the South home, many still struggle to access and utilize healthcare services, revealing critical gaps in the system. This study investigated older Asian Americans’ access to healthcare services, utilization patterns, and quality of healthcare services received. We interviewed 15 Asian Americans, aged 50 or older, residing in West Alabama. Individual semi-structured interviews were conducted either virtually or in-person in English between July and November 2023. Each interview lasted 60-90 minutes. All interviews were recorded, transcribed, and thematically analyzed using open coding. Our analysis revealed six main themes on older Asian Americans’ healthcare services utilization: 1) dual cultural influences on healthcare service expectations, shaped by both the country of origin and the U.S., 2) hesitancy taking medication due to concerns about over-prescription, 3) long waiting and delayed care, and 4) navigating different healthcare logistics and high medical expenses; nearly all participants reported paying at check-out in their country of origin, where healthcare costs are known upfront. However, in the U.S., medical bills often arrive weeks or months later, making it difficult to anticipate and understand any out-of-pocket costs. Additional challenges addressed 5) hesitancy to ask questions during check-ups and 6) perceived discrimination from healthcare service providers. These challenges often lead to underutilization of services and unmet healthcare needs in the community. Our findings highlight critical needs for culturally tailored healthcare policies and targeted interventions to eliminate disparities, enhance accessibility, and improve health outcomes for older Asian Americans.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".