Role of Medical Students as Interpreters in Bridging Language Barriers across Academic Healthcare Centers: Scoping Review
Bibliographic record
Abstract
Background: Linguistic concordance between healthcare providers and patients is critical for ensuring quality healthcare. Professional interpretation can be expensive and challenging to access. This scoping review aimed to explore the evidence on the role and education of medical students as interpreters in caring for patients with limited language proficiency (LLP), and to determine the benefits and risks associated with this practice. Methods: A scoping review using the Joanna Briggs Institute methodology was conducted. Six literature databases were searched systematically between 1946 – 02 Aug 2023. All publications discussing the use of medical students as interpreters in healthcare settings were included. Retained documents were analyzed using Covidence, with coding by two raters and regular team discussions. A thematic analysis framework was used. Results: Thirteen articles met the eligibility criteria. Multilingual medical students are frequently asked to interpret in healthcare settings. This was found to be advantageous in reducing communication barriers, improving care quality, and contributing to students’ clinical experience. Concerns were raised regarding the lack of knowledge on the professional obligations of interpreters. Interpretation training programs for medical students have been implemented at selective healthcare centres and demonstrated successful results in providing care to LLP patients. Conclusions: Medical students play an important role in addressing language barriers in healthcare institutions when serving LLP patients, by combining their unique position in the healthcare team with their medical, linguistic, and cultural competency skills. Academic institutions stand to benefit from offering interpretation training programs and integrating medical students as a resource towards delivering language-concordant care.
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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 | 0.033 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".