Measuring trust in medical research: Perspectives from racial and ethnic communities underrepresented in research
Bibliographic record
Abstract
Introduction: Underrepresentation of diverse populations in medical research undermines generalizability, exacerbates health disparities, and erodes trust in research institutions. This study aimed to identify a suitable survey instrument to measure trust in medical research among Black and Latino communities in Baltimore, Maryland. Methods: Based on a literature review, a committee selected two validated instruments for community evaluation: Perceptions of Research Trustworthiness (PoRT) and Trust in Medical Researchers (TiMRs). Both were translated into Spanish through a standardized process. Thirty-four individuals participated in four focus groups (two in English, two in Spanish). Participants reviewed and provided feedback on the instruments' relevance and clarity. Discussions were recorded, transcribed, and analyzed thematically. Results: Initial reactions to the instruments were mixed. While 68% found TiMR easier to complete, 74% preferred PoRT. Key discussion themes included the relevance of the instrument for measuring trust, clarity of the questions, and concerns about reinforcing negative perceptions of research. Participants felt that PoRT better aligned with the research goal of measuring community trust in research, though TiMR was seen as easier to understand. Despite PoRT's lower reading level, some items were found to be more confusing than TiMR items. Conclusion: Community feedback highlighted the need to differentiate trust in medical research, researchers, and institutions. While PoRT and TiMR are acceptable instruments for measuring trust in medical research, refinement of both may be beneficial. Development and validation of instruments in multiple languages is needed to assess community trust in research and inform strategies to improve diverse participation in research.
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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 | 0.106 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".