Élaboration de supports audiovisuels intégrant une formation à l'empathie pour les internes de médecine générale en Poitou-Charentes
Bibliographic record
Abstract
Introduction Empathy is a necessary skill for general practitioners. It would reduce burnout and strengthen the doctor-patient relationship. Yet, empathy is still poorly addressed in the physicians studies, who unconsciously weaken their empathy skills over the years. Objective The main objective was to create an educational tool that could fit into empathy training for general medical residents in Poitou Charente. The aim was to improve the professionalism of general practitioners. Methodology The Delphi method was used. A group of 21 experts were involved, mainly general practitioners residents. Three stages had to take place in order to obtain three consultation scenarios between a general practitioner and a patient, himself a healthcare professional. The aim was to identify differences between doctors' sympathy or empathy expression. First, three scenario topics were selected. Then each scenario was written, using the Calgary Cambridge grid. Finally, the experts were asked to validate these scenarios using the Likert scale. Results Three Delphi rounds discussions were needed to select the three topics of the first stage. When reading the database that included the experts proposals, every recurring item was built in the scenario. The written texts were validated in the first round of the third phase, with an average of over 7/10 for each. Discussion Despite a time-consuming method, only 5 experts ended up withdrawing from the study, 3 of whom were non-physicians. Convergence in the ideas gathered from the group, as well as proofreading from two researchers helped limiting the information analysis bias. Conclusion The development of empathy training in the context of medical studies will enable residents to tackle difficult consultations with appropriate communication tools, while protecting themselves from the burnout that sympathy can induce.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".