Patient participation in clinical trials conducted by principal investigators who speak one or more language(s) beyond english: Exploring ethnicity as proxy for language
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.169 | 0.778 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it