L’éducation interprofessionnelle des équipes de soins critiques par la simulation : concept, mise en œuvre et évaluation
Bibliographic record
Abstract
La simulation interprofessionnelle est une technique pédagogique efficace pour développer les compétences non-techniques en soins critiques et renforcer la collaboration interprofessionnelle des équipes afin d’améliorer la qualité des soins et le devenir du patient. L’implémentation de la simulation interprofessionnelle en formation initiale et continue est facilitée par un « réfèrent simulation » dans chaque discipline/profession afin de motiver, planifier et coordonner les équipes. Il est essentiel lors d’une simulation interprofessionnelle de considérer les aspects sociologiques (hiérarchie, pouvoir, autorité, conflits interprofessionnels, genre, accès à l’information, identité professionnelle) qui peuvent affecter la communication interprofessionnelle et le travail d’équipe mais également les processus d’apprentissage. Des outils d’évaluation spécifique du travail d’équipe lors des formations par simulation interprofessionnelle doivent être utilisés pour aider à structurer le débriefing et améliorer la performance des équipes. Le lieux de la simulation interprofessionnelle (in-situ ou intra-centre) doit servir les objectifs pédagogiques tout en intégrant la disponibilité de l’équipe et des locaux de l’unité de soins. Interprofessional simulation-based education is effective for learning non-technical critical care skills and strengthening interprofessional team collaboration to optimize quality of care and patient outcome. Implementation of interprofessional simulation sessions in initial and continuing education is facilitated by a team of “champions” from each discipline/profession to ensure educational quality and logistics. Interprofessional simulation training must be integrated into a broader interprofessional curriculum supported by managers, administrators and clinical colleagues from different professional programs. When conducting interprofessional simulation training, it is essential to account for sociological factors (hierarchy, power, authority, interprofessional conflicts, gender, access to information, professional identity) both in scenario design and debriefing. Teamwork assessment tools in interprofessional simulation training may be used to guide debriefing. The interprofessional simulation setting (in-situ or simulation centre) will be chosen according to the learning objectives and the logistics.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".