Medical Student Second Language Abilities and Confidence in Clinical Use: Mandarin Pilot
Bibliographic record
Abstract
OBJECTIVE: Language barriers are a common obstacle for medical students in multicultural centers. Students may have conversational skills in non-English languages, but find it cumbersome to use these languages in a clinical setting. We investigated the demand for resources to enhance medical communication across language barriers and the role for workshops in achieving this purpose. METHODS: Mandarin workshops and a phrasebook with medical terms were created and delivered to medical students, along with the opportunity to practice at a blood pressure clinic in the community. Surveys of medical students before and after the workshops, and three months following, were collected to determine the impact of different resources. Community members attending the blood pressure clinic were surveyed to gauge their preferences. RESULTS: Among medical student respondents, 86% spoke a non-English language, but only 24% were at least quite a bit confident in communicating with patients in a non-English language. After the Mandarin workshops, 82% of participants reported perceived benefits to their confidence in communicating with patients in Mandarin, and the phrasebook and peer coaching in Mandarin were rated as the most useful resources. Mandarin-speaking community members reported they would be more comfortable seeing providers who had learned basic Mandarin (7.5/10) compared to none at all (4.4/10). CONCLUSION: Medical students’ confidence communicating in Mandarin can be bolstered with resources including workshops and phrasebooks. This approach could be used for other languages to improve communication and contribute to more satisfying, effective and comfortable care for patients with limited English.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".