Strategies to reduce language barriers in clinical research: A national survey of pediatric health researchers
Bibliographic record
Abstract
Objectives To evaluate pediatric health researchers’ access to language services, identify barriers to including participants with limited-English proficiency (LEP), and assess the perceived importance of their inclusion and the availability of institutional support. Methods A cross-sectional survey study was conducted with pediatric health researchers across Canada. Those who conduct child health research and communicate directly with study participants through verbal and/or written communication were invited to participate. Results We received 146 responses to the survey. A key barrier to including participants with LEP was insufficient funding for language services. Only 25.3% of survey respondents reported having access to free interpretation services for research purposes through their institution, and just 16.4% had access to free translation services. Despite 91.5% of researchers acknowledging the importance of including participants with LEP, 72.6% admitted to excluding them at least some of the time. Additionally, 42.5% fail to provide accommodations for participants with LEP in their research. Time was also a major barrier to including participants with LEP in research as 48.6% of participants identified it as one of their top barriers in addition to costs of language services. Conclusions This study underscores the gap between researchers' recognition of the importance of including participants with LEP and their practices. Cost and time are major barriers, highlighting the need to improve access to language services and resources. Future research should focus on strategies to bridge this gap and promote equitable inclusion of participants with LEP.
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.
How this classification was reachedexpand
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 | 0.367 | 0.552 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.013 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".