An equity timeout in quality improvement medical education
Bibliographic record
Abstract
are comfortable interpreting medical terminology for patient encounters.Such fluency may improve with the appropriate educational resources. | WHAT WAS TRIED?We developed MyLingual MD, an educational resource available as a free mobile application (app) for bilingual clinicians to become more fluent in the shared language of their patients.The pilot version of the app includes 635 medical terms and phrases translated into 14 different non-English languages.This content was generated by compiling multiple clinical teaching resources and incorporating experiences from clinicians.Consensus was reached of the most common English medical terms and phrases used by physicians during patient encounters.The content was translated by 75 bilingual medical learners and clinicians.The translations were reviewed for accuracy and appropriateness of word choice by healthcare practitioners who trained in those languages or spoke to patients in those languages.The current version of the app allows users to learn and practice improving their language fluency through flashcards. | WHAT LESSONS WERE LEARNED?We learned multiple lessons about language equity and the development of an educational tool for clinicians.First, through discussion with stakeholders while building the app, including interpretation service providers and researchers in language barriers in healthcare, we learned that the accessibility of interpretation services is lacking in certain parts of Canada.There was also no single comprehensive resource to support clinicians who are interested in learning to communicate with patients in a shared language.Second, we learned that a mobile application allowed for the resource to be easy for clinicians to use at their convenience and allowed rapid and wider accessibility.Around 2 years since launching MyLingual MD, we have made substantial outreach with a total of 645 downloads (528 in Canada, 77 in USA, and 40 in other countries).Third, participating medical students and app users noted improved vocabulary in their respective languages and greater appreciation for the nuances when communicating with patients in their native tongue.Furthermore, the connections established between clinicians and medical students may serve as potential mentorship opportunities to support new initiatives within their respective cultures.Every progressive step in the project has built a network of individuals seeking to optimise their communication skills to provide higher quality care.MyLingual MD optimises cultural diversity to transform language from being an accessibility barrier into a bridge for strengthened patient-physician relationships.
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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 | 0.063 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.059 | 0.010 |
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".