Enseigner en situation de pandémie : La transformation de l’enseignement et de la supervision clinique
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic is an event that deeply impacts our personal, professional, and collective lives. How do we teach in these times of great upheaval? What are the main changes that have occurred? Method: Using the Cartel logic, four professors and a qualitative researcher carried out an autoethnographic research aimed at documenting the main changes that have occurred in the teaching of family medicine in their respective practices located in four different academic family medicine groups at the University of Sherbrooke. Results: Five key moments in teaching that occurred during a pandemic were identified: a) the declaration of a pandemic, b) the approach with the graduating/advanced cohort of residents, c) the anticipation and preparation for the arrival of new residents, d) arrival of first year residents and e) adaptation to the second wave. For each moment, we present the issues encountered in our care and teaching practices under three transversal relational axes: the relationship of humans to their cultural context, the patient-doctor relationship, and the teacher-resident relationship. Conclusion: Our analysis shows that the transmission of medical knowledge and the art of medicine cannot take place without specific attention to the overall cultural context, the contextual relationship of clinical care, and the teaching relationship. Our study also makes it possible to recommend the opening of spaces for reflection and dialogue in our teaching environments.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".