Research Participant‐Centered Outcomes at NIH‐Supported Clinical Research Centers
Bibliographic record
Abstract
BACKGROUND: Although research participation is essential for clinical investigation, few quantitative outcome measures exist to assess participants' experiences. To address this, we developed and deployed a survey at 15 NIH-supported clinical research centers to assess participant-centered outcomes; we report responses from 4,961 participants. METHODS: Survey questions addressed core aspects of the research participants' experience, including their overall rating, motivation, trust, and informed consent. We describe participant characteristics, responses to individual questions, and correlations among responses. RESULTS: Respondents broadly represented the research population in sex, race, and ethnicity. Seventy-three percent awarded top ratings to their overall research experience and 94% reported no pressure to enroll. Top ratings correlated with feeling treated with respect, listened to, and having access to the research team (R(2) = 0.80-0.96). White participants trusted researchers more (88%) than did nonwhite participants collectively (80%; p < 0.0001). Many participants felt fully prepared by the informed consent process (67%) and wanted to receive research results (72%). CONCLUSIONS: Our survey demonstrates that a majority of participants at NIH-supported clinical research centers rate their research experience very positively and that participant-centered outcome measures identify actionable items for improvement of participant's experiences, research protections, and the conduct of clinical investigation.
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 | 0.076 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".