Motivation to Participate in Precision Health Research and Acceptability of Texting as a Recruitment and Intervention Strategy Among Vietnamese Americans: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The largest effort undertaken in precision health research is the Precision Medicine Initiative (PMI), also known as the All of Us Research Program, which aims to include 1 million or more participants to be a part of a diverse database that can help revolutionize precision health research studies. Research participation from Asian Americans and Pacific Islanders in precision health research is, however, limited; this includes Vietnamese Americans, especially those with limited English proficiency. PMI engagement efforts with underserved communities, including members of minority populations or individuals who have experienced health disparities such as Vietnamese Americans with limited English proficiency, may help to enrich the diversity of the PMI. OBJECTIVE: The aim of this study is to examine the attitudes towards and perceptions of precision health, motivations and barriers to participation in precision health research, and acceptability of SMS text messaging as a recruitment and intervention strategy among underserved Vietnamese Americans. METHODS: A community sample of 37 Vietnamese Americans completed a survey and participated in one of 3 focus groups classified by age (18-30, 31-59, and ≥60 years) on topics related to precision health, participation in precision health research, texting or social media use experience, and insights on how to use text messages for recruitment and intervention. Participants were recruited via community organizations that serve Vietnamese Americans, flyers, word of mouth, and Vietnamese language radio announcements. RESULTS: Most participants had little knowledge of precision health initially. After brief education, they had positive attitudes toward precision health, although the motivation to participate in precision health research varied by age and prior experience of research participation. The main motivators to participate included the desire for more knowledge and more representation of Vietnamese Americans in research. Participants were open to receiving text messages as part of their research participation and provided specific suggestions on the design and delivery of such messages (eg, simple, in both English and Vietnamese). Examples of barriers included misinterpretation of messages, cost (to send text messages), and preferences for different texting platforms across age groups. CONCLUSIONS: This study represents one of the first formative research studies to recruit underserved Vietnamese Americans to precision health research. It is critical to understand target communities' motivations and barriers to participation in research. Delivering culturally appropriate text messages via age-appropriate texting and social media platforms may be an effective recruitment and intervention strategy. The next step is to develop and examine the feasibility of a culturally tailored precision health texting strategy for Vietnamese 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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".