Clinical trial recruitment of people who speak languages other than English: a Children’s Oncology Group report
Bibliographic record
Abstract
BACKGROUND: Persons who speak languages other than English are underrepresented in clinical trials, likely in part because of inadequate multilevel resources. We conducted a survey of institutions affiliated with the Children's Oncology Group (COG) to characterize current research recruitment practices and resources regarding translation and interpretation services. METHODS: In October 2022, a 20-item survey was distributed electronically to institutions affiliated with COG to assess consent practices and resources for recruiting participants who speak languages other than English to COG trials. Descriptive statistics were used to summarize responses; responses were compared by institution size and type as well as respondent role. RESULTS: The survey was sent to 230 institutions, and the response rate was 60% (n = 139). In total, 60% (n = 83) of those respondents had access to short-form consent forms. Full consent form translation was required at 50% of institutions, and 12% of institutional review boards restricted use of centrally translated consent forms. Forty-six percent (n = 64) of institutions reported insufficient funding to support translation costs; 19% (n = 26) had access to no-cost translation services. Forty-four percent (n = 61) were required to use in-person interpreters for consent discussions; the most frequently cited barrier (56%) to obtaining consent was lack of available in-person interpreters. Forty-seven percent (n = 65) reported that recruiting persons who speak languages other than English to clinical trials was somewhat or very difficult. CONCLUSIONS: Institutions affiliated with COG face resource-specific challenges that impede recruitment of participants who speak languages other than English for clinical trials. These findings indicate an urgent need to identify strategies aimed at reducing recruitment barriers to ensure equitable access to clinical trials.
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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 | 0.037 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".