Multilevel challenges to equitable inclusion of children in trials when parents use languages other than English: A qualitative report from Children's Oncology Group's Diversity and Health Disparities Committee Language Equity Working Group
Bibliographic record
Abstract
BACKGROUND: Increasing representation in clinical trials is a priority for the National Cancer Institute and Children's Oncology Group (COG). Our survey of COG-affiliated institutions revealed that many sites have insufficient processes and resources to enroll children whose parents use languages other than English (LOE). We describe reported barriers and facilitators to enrolling children in clinical trials when parents use LOE and propose opportunities for improvement. PROCEDURES: We sent a 20-item survey to COG-affiliated institutions. Five items allowed respondents to expand on replies to questions about (a) local institutional review board (IRB) requirements regarding translation of consent documents, (b) contributors to provider discomfort consenting parents who use LOE, (c) available language services and resources, and (d) barriers to enrolling children whose parents use LOE or offer ideas about approaches to improvements. Two pairs of researchers independently coded free-text responses and compared results for concordance. RESULTS: A total of 139 (N = 230; 60%) institutions returned the survey. Respondents were mainly physician principal investigators (n = 79/139; 57%) at the United States sites (n = 118/139; 85%) serving less than 100 newly diagnosed children per year (n = 99/139, 71%). They described challenges at multiple levels. Proposed approaches to improvements included centralized provision of translated materials and video educational materials in various languages, and collaborating with IRBs on regulatory processes that protect families and facilitate equitable clinical trial access. CONCLUSIONS: Clinical trial consortia, such as COG, face challenges in enrolling representative samples. Further research is required to design and implement multilevel interventions to ensure equitable access for all, regardless of language used, and mitigate disparate research participation.
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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 | 0.064 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".