Patient participation in clinical trials conducted by principal investigators who speak one or more language(s) beyond english: Exploring ethnicity as proxy for language
Bibliographic record
Abstract
Background: To explore the association between ethnicity, as a proxy for language, and participation in clinical trials (CT) conducted by Principal Investigators (PI) who speak one or more language in addition to English. Methods: This retrospective, descriptive study utilized CT participant demographic data extracted from the largest Midwestern non-profit healthcare system between January 1, 2019 and 12/31/2021. The CT participant sample (N = 4308) was divided for comparison: CT Participants of Hispanic or Latino Origin (N = 254; 5.90 %) and CT Participants of Non-Hispanic or Latino Origin (N = 4054; 94.10 %). Logistic regressions were performed to generate the crude and adjusted odds of patients of Hispanic or Latino origin participating in CTs conducted by PIs who speak another language in addition to English. Results: Crude analysis revealed that patients of Hispanic or Latino ethnicity had 2.04 (1.58, 2.64) times greater odds of participating in CTs conducted by PIs who speak another language than English (<0.0001), which increased to 2.67 (1.97, 3.62) times greater odds after adjusting for sex, race, age and insurance (p < 0.0001). Conclusions: Overall findings indicate that patients of Hispanic or Latino ethnicity, who are more likely to speak Spanish, have greater odds of participating in CTs conducted by PIs who speak another language beyond English. This may imply that cultural sensitivity at the top of a CT study team, as likely to be demonstrated by PIs who speak another language beyond English, may be an important contributor to reducing ethnicity- and language-based barriers to diversity in CTs and a relationship worth exploring further.
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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 | 0.036 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".