L’influence du discours du médecin sur l’apparition des motifs de consultation secondairement révélés : analyse conversationnelle à partir d’enregistrements audio de consultations en médecine générale
Bibliographic record
Abstract
Introduction: medical consultation is a meeting between a doctor and a patient seeking care, which he seeks by expressing a reason for consultation. The patient's initial request is not necessarily his main concern. Aim: determine the different communication techniques used in the consultation that would promote the expression of secondarily revealed reasons and establish when in the session these reasons are revealed. Methods: a descriptive qualitative study was carried out using a database of audio recordings of 72 consultations carried out by three general practitioners in three private practices in Gironde. The verbatims obtained by manual transcription were analysed using the double coding method with data triangulation. We have established a communication score according to the Calgary-Cambridge guide. Results: Secondarily revealed reasons are mainly stated during the questioning. There are mostly communication techniques such as facilitation, open and closed questions, reformulation, clarification, summary, empathy, active involvement, legitimation, humor and small talk. Conclusion: to create a climate of trust within a consultation, the learning of communication, as a complex skill, must therefore be integrated into the training of becoming a doctor. He will be able to use the Calgary-Cambridge grid to conduct his interview and obtain an optimal relationship with his patients.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".