Training Medical Students to Communicate with a Linguistic Minority Group
Bibliographic record
Abstract
Effective communication is central to a successful physician-patient relationship. Communication is usually enhanced when the linguistic and cultural attributes of patients are incorporated in health care delivery. With this purpose in mind, the Faculty of Medicine at the University of Ottawa has developed a French-language stream to train future physicians for the francophone minority population of Ontario. As part of this project, a communication skills laboratory was created for francophone students in 1996, since all three tertiary care teaching hospitals operated in English only. The laboratory consists of a controlled environment where francophone students conduct interviews in French while being observed by clinicians trained in observation and feedback techniques. It makes use of simulated patients trained to play specific roles and to give feedback to students. Laboratory sessions take place throughout the first and second years and expose students to 15 scenarios covering different themes in each year. Each scenario includes a communication problem. Facilities are in place for filming the encounters for review by students. The project has had favorable outcomes. Both students and clinician-supervisors find that the laboratory offers an excellent learning environment and describe the cases as realistic and instructive. Clerkship preceptors are pleased with the students' communication skills. Because of the success of the laboratory, faculty authorities plan to translate the scenarios and offer similar sessions to students in the English-language stream. The teaching methods used in the communication skills laboratory may be of interest to other medical schools that serve linguistic minority populations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".