Notice bibliographique
Résumé
We thank Dr. Rubio and colleagues for their interest in our work.1 We agree that effective education involves alignment between the clinical task and the simulation task (i.e., “functional task alignment”). We introduced this term to get around the problems associated with the term “fidelity” that we found in the literature, and to highlight the level of analysis necessary for designing effective simulation training. Briefly, functional task alignment involves identifying the essential constructs of the target task and aligning them with the elements of the simulator to be used for training. We feel this is a critical part of the process for designing effective simulation training sessions. We appreciate Dr. Rubio and colleagues’ illustration about how humans think about reality, but adoption of this particular theoretical viewpoint is not necessary for explaining the factors in volved in effective transfer of learning. To paraphrase, the authors state that simulation should capture the imagination, trigger physiological res ponses, and tap into participants’ history. In short, educational effectiveness depends critically on the way in which learners engage with the educational material, based on their prior experience. This is a fundamental tenet of constructivism, which emphasizes the motivational power that can be drawn from the learner’s appreciation of the relevance of the current lesson to the learner’s unique prior history. Thus, in principle, learner orientation can be managed to emphasize particular expectations about how the simulator aligns with future performance in the applied setting. In this way, effective orientation of the learner to the simulator can create a relevant “prior history.” In short, the learner can “project” fidelity onto the simulator depending on their unique learning objectives. In our experience in this field, we have seen highly effective educational impact using simple physical design elements. Technological advances are obviously needed in education, but we need to understand why and when to use technology to enhance learning. Key questions for future research include (1) Under what conditions do low-tech simulators confer benefit? (2) What role does learner engagement and sus pension of disbelief play in effective simulation-based training? (3) How do learner preferences regarding technology affect engagement and effectiveness of learning? (4) How can task analysis help in determining simulator technology requirements? and (5) How can we help resource-poor facilities take advantage of research showing the benefit of low-tech simulators? Stanley J. Hamstra, PhD Professor of medicine and director, Academy for Innovation in Medical Education, Faculty of Medicine, University of Ottawa, and research director, University of Ottawa Skills and Simulation Centre, Ottawa, Ontario, Canada; [email protected] Ryan Brydges, PhD Assistant professor of medicine, University of Toronto, Toronto, Ontario, Canada. Rose Hatala, MD Associate professor of medicine, University of British Columbia, Vancouver, British Columbia, Canada. David A. Cook, MD Professor of medicine and medical education, Mayo Clinic College of Medicine, and director, Office of Education Research, Mayo Medical School, Rochester, Minnesota.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,090 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,006 |
| Communication savante | 0,007 | 0,011 |
| Science ouverte | 0,006 | 0,004 |
| Intégrité de la recherche | 0,046 | 0,076 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,011 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».