Impact d’une formation de professionnalisme sur les réponses au stress pendant une situation critique : essai contrôlé en simulation chez des internes d’anesthésie-réanimation
Bibliographic record
Abstract
Introduction: Repetition of stressful events during a medical career is source of psychosocial risks. Prevention and training strategies are important to prevent long-term consequences of repeated stressful situations. Method: A pedagogical team from the Anesthesia and Intensive Care Unit set up a specific training course (teaching professionalism and improving quality of life) for first-year Grenoble anesthesia intensive care residents (13 sessions of 2 hours each spread over 6 months). The aim of this study was to evaluate the effectiveness of this training to develop psychosocial skills of anesthesia residents early in their internship. The study compared two groups: a group that did receive the training (1st class of anesthesia residents) and a group that did not (2nd class of anesthesia residents). Participants from both groups participated in a high-fidelity simulation session during which data were collected: stress response scores (STAI-YA, EVA-Stress, EVA-REC, SDNN), crisis resource management (Ottawa), and quality of communication with the family (GRIEV-ING). Epidemiological data were collected outside the simulation: quality of life scale (WHOQoL), burnout (MBI), and anxiety-personality trait (STAI-YB). Results: The results (n=20) showed a decrease in situational anxiety in acute stress situations between the trained and untrained groups. There was no difference between the two groups regarding crisis resource management and quality of communication with the family. At the end of the first year of internship, there was no difference between the two groups regarding quality of life, burnout, and trait anxiety-personality. Discussion: The training conducted on professionalism may have contributed to attenuating the anxiety response of anesthesia-intensive care residents to a stressful situation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".