Bridging the gap: Understanding Latino willingness to participate in public health and clinical trials research across diverse subgroups
Bibliographic record
Abstract
Background: Latino sub-groups remains limited. The purpose of this study was to investigate how knowledge, awareness and willingness to participate in research differs between US- born and immigrant Latinos. Methods: We conducted a population-based household telephone survey with Latino adults (N = 1264), with 68 % Mexican/Mexican American, 11 % Central/South American, 8 % Puerto Rican and the remaining 13 % self-identified as "Other". The "Building Trust Survey," included valid standardized instruments designed to assess knowledge of research, human subjects' protections, previous participation in research, immigrant status (nativity), length of time in the US, and country of origin. Results: The study found that Latinos who immigrated to the US as teens or young adults were more willing to participate in medical research than those born in the US. Willingness to "take" something in a study varied by Latino subgroup, immigration age, gender, and age. Analysis highlighted that Mexican/Mexican Americans (76 %) and Central/South Americans (74 %) indicated a willingness to participate in research but also were less likely to have been "Asked" to participate in research (9 % and 6 % respectively) compared to the other subgroups (p < .05). Conclusions: Insights from this study will inform the development of culturally tailored interventions aimed at successfully recruiting and retaining Latino populations in public health and clinical trials research, thereby contributing to more equitable and representative health outcomes.
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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 | 0.129 | 0.214 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".