Developing Genre-Specific Teaching Materials for the <i>Anamnèse </i>in French for Health Sciences
Bibliographic record
Abstract
The anamnèse, or case history, is a foundational written genre in healthcare, produced routinely by professionals during clinical encounters. Despite its importance, there is a notable lack of pedagogical resources tailored to support non-French-speaking students in mastering this genre within French for Specific Purposes (FSP) contexts. This gap presents a challenge for instructors, who often must create custom materials in collaboration with healthcare practitioners. Building on a previous needs-based study of course content, this teaching report documents the development of targeted instructional materials for the anamnèse genre within a French for Health Sciences course at an English-language university in Montreal, Canada. The development process began with a review of relevant literature and textbooks in both French and English for the health sciences. Following this, and after meeting with healthcare faculty members, in the course of which students’ writing challenges during medical placements were idenified, the teaching materials were created. The findings led to the formulation of a proposed structure for the anamnèse, grounded in both practitioner input and existing literature on case histories in English and French. Sample teaching activities are presented to demonstrate how the materials address learners’ specific needs. This report contributes to the advancement of genre-based pedagogy in FSP and emphasizes the value of interdisciplinary collaboration in developing effective, context-sensitive teaching tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".