Language interpretation and translation in emergency care: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Patients with preferred languages other than English face barriers to communication and access to appropriate care in English-speaking emergency care systems, leading to poorer communication and quality of care, as well as increased rates of investigations and healthcare utilization. While professional interpretation can help bridge this gap, uptake is exceedingly poor, suggesting the need for enhanced implementation and more accessible modalities. Our study will map the existing literature on interpretation/translation in emergency care, with a focus on the breadth of modalities, barriers/facilitators to implementation, and effectiveness/implementation outcomes. METHODS: We will conduct a scoping review based on the Joanna Briggs Institute methodology. We will search MEDLINE, Embase, PsycINFO, CINAHL, Scopus, iPortal, Native Health Database and Cochrane Library CENTRAL for articles from inception to May 2024 without any language or country restrictions. Primary research articles involving interpretation/translation between English and a non-English language during emergency healthcare encounters will be included. Screening and data extraction will be completed by two independent team members. Results will be descriptively summarized and barriers/facilitators to implementation will be mapped according to the Consolidated Framework for Implementation Research. STAKEHOLDER ENGAGEMENT & KNOWLEDGE TRANSLATION: Results will be disseminated at academic conferences and published in a peer-reviewed journal. We will share our key findings via a graphical abstract and social media campaign. Our team includes our provincial health authority interpretation services lead who brings lived experience and will inform and validate our results and help identify future areas of needed research. They will also help us identify key messages and appropriate methods for dissemination to maximize knowledge translation to patients/families, local policy/clinical practice, as well as funding agencies.
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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 | 0.157 | 0.116 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.078 | 0.021 |
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".