Language Access and Health Equity: Role of Multilingual Administrative Staff in Reducing Healthcare Disparities Among South Asian Immigrant Communities in The U.S.
Bibliographic record
Abstract
This study examined the role of multilingual administrative staff in reducing healthcare disparities and improving health equity among South Asian immigrant communities in the United States using a quantitative, cross-sectional design. A total of 412 participants were included in the final analysis after data validation, representing diverse linguistic, socioeconomic, and migration backgrounds. The study assessed the impact of language-concordant administrative interaction on key outcomes including healthcare access efficiency, patient satisfaction, communication clarity, and healthcare utilization patterns. Descriptive findings indicated that 58.7% of participants reported access to multilingual administrative staff, while 35.9% had limited English proficiency, highlighting the relevance of language access in this population. Comparative analysis revealed that participants with multilingual administrative support demonstrated significantly higher healthcare access scores (M = 4.12, SD = 0.68) compared to those without support (M = 3.21, SD = 0.81). Patient satisfaction scores were also higher in the multilingual group (4.25 vs. 3.34), while missed appointment rates were substantially lower (12.8% vs. 28.9%). Preventive care utilization was greater among participants with language-concordant support (67.4% vs. 48.2%), indicating improved engagement with healthcare services. Multivariable regression analysis confirmed that multilingual administrative staffing was a significant predictor of patient satisfaction (β = 0.41, p < 0.001) and access efficiency (β = 0.36, p < 0.001) after controlling for demographic and socioeconomic variables. Subgroup analysis showed stronger effects among individuals with limited English proficiency, recent immigrants, and those with lower education levels, with satisfaction improvements exceeding +1.0 mean difference in these groups. Effect size analysis indicated large effects for patient satisfaction (d = 0.91) and communication clarity (d = 0.84), and moderate effects for access efficiency (d = 0.68). These findings demonstrated that multilingual administrative staff significantly improved both operational and patient-centered outcomes, providing strong empirical evidence that administrative-level language access plays a critical role in reducing healthcare disparities and enhancing equitable healthcare delivery among South Asian immigrant populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".