42 Language interpretation and translation in emergency care: A scoping review
Bibliographic record
Abstract
Abstract Background Families with preferred languages other than English experience poorer communication and care quality in English-speaking emergency settings. While professional interpretation can bridge this gap, uptake is sparse, suggesting the need for improved implementation and more accessible modalities. Objectives We sought to map the existing literature on interpretation/translation in emergency care, with a focus on modalities, barriers/facilitators to implementation, and outcomes used. Design/Methods Utilizing the Joanna Briggs Institute methodology, we conducted a scoping review and searched 8 databases 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 were included. Screening and data extraction were completed by two team members. Results were descriptively summarized and barriers/facilitators to implementation were mapped according to the Consolidated Framework for Implementation Research (CFIR). Results Out of 1809 search results, 82 studies were included, of which 12 (15%) were randomized controlled trials. The majority of studies (70/82, 85%) were single centre and 30 (37%) included children. No studies directly asked paediatric patients about their perspectives. Professional telephonic interpretation was the most commonly studied modality (50/82, 61%). Only four studies (4.9%) examined websites/apps and only one study (1.2%) examined simultaneous interpretation. Mapped to the CFIR, barriers and facilitators primarily centered around information technology infrastructure, resources, and access to knowledge in the local healthcare environment, as well as the perceptions and motivations of healthcare workers and patients. 16 (20%) studies examined healthcare utilization outcomes and 22 (27%) examined communication/interpretation accuracy. Amongst implementation outcomes, only adoption (58/82, 71%) and acceptability (19/82, 23%) were consistently examined (n≥10 studies). Conclusion Interpretation/translation in emergency care has mostly been examined through single centre studies, with few randomized trials and paediatric studies. Importantly, the experience of children themselves has yet to be explored. Information technology infrastructure and resources were some of the most commonly cited barriers and facilitators to language interpretation/translation, highlighting the need to expand testing and implementation of novel modalities. Future studies should consider the unique experiences and needs of both children and their caregivers.
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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 | 0.066 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.026 | 0.027 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".