Chapitre 10 . La coconstruction d’un cadre d’apprentissage en éthique par une équipe apprenante : une démarche pédagogique en éthique
Bibliographic record
Abstract
This article describes the experience of a work team and the process of developing a learning framework for professional ethics in healthcare. This framework is designed to help healthcare professionals to deal with complex clinical or research situations. Our interdisciplinary team developed a framework that identifies three interrelated learning objectives. These objectives are divided into key elements, considering the complexity of professional action and the autonomy of the learner. The framework also includes a progression in the development of these objectives. It serves as an essential tool to design and evaluate ethics-focused learning tailored to the pedagogical context and organization of various clinical and scientific programs. In the experience of its coconstruction, the team has seen the richness and challenges of operating in a posture that is both learning and ethical. Making clearer the team's experience by combining these two perspectives will enable other teams to draw inspiration from it. This dialogical, iterative approach, nourished by the diversity of individual perspectives, has led to an evolution in ethics teaching knowledge. It supports innovation in training curricula based on this framework.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".