La responsabilité sociale en santé des médecins : analyse du premier tour d’un Delphi international
Bibliographic record
Abstract
INTRODUCTION: The definition of social accountability in health (SAH) for medical schools has raised the issue of extending its understanding to health professions. To respond to this challenge, the Réseau international francophone pour la responsabilité sociale en santé (RIFRESS) first focused on physicians, all specialties combined. PURPOSE OF THE STUDY: This article presents the first open round of a Delphi study aimed at exploring the manifestations expected of a physician engaged in SAH, according to the opinion of an international French-speaking group of medical training experts. An intentional sample of experts was invited to participate from the list of RIFRESS members to ensure diversity in relation to certain characteristics. The first round of the Delphi consisted of a questionnaire made up of open questions inviting participants to describe the specific manifestations of a physician whom they consider to be engaged in SAH. The responses were then subjected to thematic analysis. RESULTS: Thirty participants presenting a diversity of profiles provided a total of 140 verbatim statements, grouped into thirteen themes. CONCLUSIONS: The themes emerging from this study refer to medical expertise skills inherent to the profession and especially to behavioral skills essential for a medical practice engaged in social accountability. The values of SAH (quality, equity, relevance, and efficiency) are at the heart of the practitioner's commitments.
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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 | 0.076 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".