Variations in racial and ethnic groups’ trust in researchers associated with willingness to participate in research
Bibliographic record
Abstract
Low enrollment in U.S. biomedical research by non-White adults has historically been attributed to mistrust, but few studies have simultaneously examined dimensions of trust in three or more racial/ethnic groups. Leveraging the racial/ethnic diversity of New Jersey, we prospectively recruited 293 adults (72% women, 38% older than 54 years of age) between October 2020 and February 2022 to complete two anonymous surveys in English or one of the common languages (e.g., Spanish, Mandarin Chinese). The first consisted of 12 Likert Scale questions related to trust in biomedical researchers (according to safety, equity, transparency), and the second assessed willingness to consider participation in eight common research activities (health-related survey, blood collection, genetic analysis, medication study, etc). Participants self-reported as Hispanic (n=102), Black (n=49), Chinese (n=48), other Asian (n=53), or White (n=41) race/ethnicity. Factor analysis showed three aspects related to trust in researchers: researchers as fiduciaries for research participants, racial/ethnic equity in research, and transparency. Importantly, we observed differences in the relationship between mistrust and willingness to participate. Whereas Chinese respondents' low trust in researchers mediated their low interest in research involving more than health-related surveys, Hispanic respondents' low trust in research equity did not deter high willingness to participate in research involving blood and genetic analysis. We caution that a generic association between trust and research participation should not be broadly assumed, and biomedical researchers should prospectively assess this relationship within each minoritized group to avoid hasty generalization.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".