Understanding Barriers to Clinical Trial Participation Among U.S. Women: A National Survey Study
Bibliographic record
Abstract
Abstract Despite the persistent underrepresentation of women—particularly those from racially and ethnically minoritized groups—in clinical research, little is known about their perspectives on participation. This study examined healthcare experiences, access, and attitudes toward clinical trials among U.S. women and assessed how race, socioeconomic status, and healthcare access intersect to shape willingness to participate (WTP). We conducted a national cross-sectional online survey (January–March 2023) of 5,301 women aged 18–70 years. The 81-item questionnaire assessed demographics, health status, healthcare access, and clinical trial experiences. Among 4,987 respondents reporting race (77% White, 14% Black, 7% Asian, 2% Other), nearly 80% expressed interest in participating in clinical trials, yet only 11% had been invited and 7% had enrolled. In adjusted models, WTP was lower among Black (β = −0.06; P = .04) and Asian (β = −0.09; P = .01) women than among White women, whereas higher educational attainment and multimorbidity predicted greater WTP. Altruism, clear study explanations, and financial compensation were key motivators, while time burden and concerns about side effects were major barriers, with the salience of these factors varying by race. Most respondents (88%) endorsed the importance of women’s inclusion and sex-specific reporting, though neutrality on these issues was more frequent among racially minoritized women. Despite high interest, structural and informational barriers continue to constrain women’s engagement in clinical research, underscoring the need for trust-building, burden-reducing, and culturally responsive strategies to promote equitable participation and improve representation across racial groups.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".