Éléments de compréhension de nouvelles formes de soin : rapport d'expérience des tandems de partenariat médecin-patient partenaire en clinique
Bibliographic record
Abstract
Since 2022, an academic organisation offering an Art of Care in partnership with patients and the public in a systemic dimension (Teaching, cure and care, health system Research and Popular Education) has been accompanying new care practices. This article sheds light on one of these new clinical practices, that of doctor-patient partnership tandems in a clinical setting. According to the authors, who come from two different countries, this insight into care partnership tandems developed both in primary care and in health care establishments makes it possible to provide elements of relevance for this new type of practice and to put forward an initial hypothesis of the context needed to scale up these new figures involved in care. Depuis 2022, une organisation académique proposant un Art du Soin en partenariat avec les patients et le public dans une dimension systémique (Enseignement, Soin, recherche et éducation populaire) accompagne de nouvelles pratiques de soin. Cet article propose d'éclairer un de ces nouveaux exercice en clinique, celui de tandems de partenariat médecin patient en milieu clinique. Cet éclairage de tandems de partenariat de soin développés tant en soins primaires qu'en établissement de santé permet selon les auteurs de deux pays différents, d'apporter des éléments de pertinence de ce nouveau type d'exercice et de poser une première hypothèse du contexte nécessaire à une mise à l'échelle de ces nouvelles figures impliqué dans le soin.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".