Improving Racial and Ethnic Equity in Clinical Trials Enrolling Pregnant and Lactating Individuals
Bibliographic record
Abstract
Racial and ethnic marginalized populations have historically been poorly represented, underrecruited, and underprioritized across clinical trials enrolling pregnant and lactating individuals. The objectives of this review are to describe the current state of racial and ethnic representation in clinical trials enrolling pregnant and lactating individuals and to propose evidence-based tangible solutions to achieving equity in these clinical trials. Despite efforts from federal and local organizations, only marginal progress has been made toward achieving equity in clinical research. This continued limited inclusion and transparency in pregnancy trials exacerbates health disparities, limits the generalizability of research findings, and may heighten the maternal child health crisis in the United States. Racial and ethnic underrepresented communities are willing to participate in research; however, they face unique barriers to access and participation. Multifaceted approaches are required to facilitate the participation of marginalized individuals in clinical trials including partnering with the local community to understand their priorities, needs, and assets; establishing accessible recruitment strategies; creating flexible protocols; supporting participants for their time; and increasing culturally congruent and/or culturally sensitive research staff. This article also highlights exemplars in pregnancy research.
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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 | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.099 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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, 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".