Improving Vietnamese Immigrants’ Cognitive Health Literacy: University-Community Partnerships
Bibliographic record
Abstract
Abstract After the fall of Saigon in 1975, waves of Vietnamese refugees/immigrants migrated to the United States (U.S.). Currently, 2.3 million Vietnamese live in the U.S. Many came with adverse conditions; however, their health data is scarce. After 50 years, many are aged and may have unmet health needs. To fill this gap, we developed the Vietnamese Aging and Care Survey and collected their health data in Houston, Texas, which revealed a high prevalence of physical, mental, and cognitive disabilities. Using the university-community partnerships, we developed a linguistically and culturally tailored dementia one-pager, formed the Cognitive Health Initiative (CHAIN), and offered Vietnamese refugees/immigrants complimentary memory tests. This study explains how the CHAIN program operates and fosters intergenerational relationships. Using the Cultural Exchange Model, we trained bilingual/bicultural Vietnamese college students (Cohort 1) and conducted cognitive assessments, Vietnamese Montreal Cognitive Assessment (V-MoCA), in 2023. In 2024, when Cohort 2 students joined, we formed mentor (Cohort 1)-mentee (Cohort 2) dyads. Cohort 1 demonstrated the assessment while Cohort 2 observed their mentors perform assessments. After several demonstrations, Cohort 2 tried the assessments supervised by Cohort 1. They repeated this sequence until Cohort 2 felt comfortable conducting the assessments independently. During 2023-2024, we recruited 42 students, attended 24 health fairs, and offered 406 V-MoCA. Mentors (Cohort 1) completed training their mentees (Cohort 2) with tips for conducting the V-MoCA and how to work with older adults. Through the mentor-mentee relationships, the CHAIN promoted teamwork, accountability, ethics, and responsibility to the community, and demonstrated successful university-community partnerships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".