PEDAGOGICAL METHODS BEHIND TEACHING THE PRACTITIONER-PATIENT INTERVIEW IN FRENCH
Bibliographic record
Abstract
The practitioner-patient interview has been the subject of several studies in the world of medicine and in the field of teaching languages for specific purposes It has been considered one of the most critical oral genres in language teaching for medical-health purposes. Every health professional has to know and understand why the patient has come for a consultation; they must have the necessary skills to obtain as much information about the patient’s health problem as possible, and if necessary, they have to perform a physical examination. The practitioner-patient interview is divided into several steps. Each step consists of a specific task with its specific objectives for the practitioner. For about fifteen years, the French Language Centre of McGill University, an English-speaking university in Montreal, Canada, has been offering French courses to students specializing in different areas of the Faculty of Health Sciences and Social Work who wish to do their clinical placements and pursue their professional career in the province of Quebec. Most of McGill’s students are native English speakers from different parts of Canada and the United States or international students whose first language is not necessarily English. One of the most important oral genres which must be taught to these students is the practitioner-patient interview in French, since one of their principal tasks as healthcare professionals will to interact with patients. Furthermore, students who have obtained a degree in any healthcare profession from an English-speaking university in the French-speaking province of Quebec must take a French language exam offered by the Office québécois de la langue française (OQLF). In one of the activities of this exam, the candidates must interview a patient or a caregiver in French. Therefore, this constitutes another reason to teach the practitioner-patient interview to our students. Unfortunately, there is little extant literature on how to teach students to carry out a practitioner-patient interview in French as a second language. Moreover, the possibility of recording actual interviews for use in class is practically impossible to respect patient confidentiality. This paper aims to share with the scientific community and with other language for specific purposes instructors how the practitioner-patient interview is taught at McGill University to non-native French speakers who wish to work in Quebec.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".