Lost in translation: a national cross-sectional study on medical interpreter use by pediatric residents
Bibliographic record
Abstract
BACKGROUND: Lack of communication in a family's preferred language is inequitable and results in inferior care. Pediatric residents provide care to many families with non-English or French language preferences (NEFLP). There is no data available about how Canadian pediatric residents use interpreters, making it difficult to develop targeted interventions to improve patient experience. OBJECTIVES: Our purpose was to assess translation services in pediatric training centers and evaluate resident perception of their clinical skills when working with NEFLP patients and families. This survey represents the first collection of data from Canadian pediatric residents about interpreter services. METHODS: Eligible participants included all pediatric residents enrolled in an accredited Canadian pediatric training program. An anonymous survey was developed in REDCap© and distributed via email to all pediatric residents across Canada. Descriptive statistics were performed in STATA v15.1. RESULTS: 122 residents responded. Interpreter services were widely available but underused in a variety of clinical situations. Most (85%) residents felt they provided better care to patients who shared their primary language (English or French), compared with families who preferred other languages-even when an interpreter was present. This finding was consistent across four self-assessed clinical skills. CONCLUSIONS: Residents are more confident in their clinical and communication skills when working with families who share their primary language. Our findings suggest that residents lack the training and confidence to provide equal care to families with varying language preferences. Pediatric training programs should develop curriculum content that targets safe and effective interpreter use while reviewing non-spoken aspects of cultural awareness and safety.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".