Mapping the Expert Mind: Integration Method for Revising the ACES Medical Simulation Curriculum
Notice bibliographique
Résumé
PURPOSE: This article shares our experience developing an integrated curriculum for the ACES (Acute Critical Event Simulation) program. The purpose of the ACES program is to ensure that health care providers develop proficiency in the early management of critically ill patients. The program includes multiple different types of educational interventions (mostly simulation-based) and targets both specialty and family physicians practicing in tertiary and community hospitals. METHODS: To facilitate integration between different educational interventions, we developed a knowledge repository consisting of cognitive sequence maps that make explicit the flow of cognitive activities carried out by experts facing different situations - the sequence maps then serving as the foundation upon which multimodal simulation scenarios would be built. To encourage participation of experts, we produced this repository as a peer-reviewed ebook. Five national organizations collaborated with the Royal College of Physicians and Surgeons of Canada to identify and recruit expert authors and reviewers. Foundational chapters, centered on goals/interventions, were first developed to comprehensively address most tasks conducted in the early management of a critically ill patient. Tasks from the foundational chapters were then used to complete the curriculum with situations. The curriculum development consisted of two-phases each followed by a peer-review process. In the first phase, focus groups using web-conferencing were conducted to map clinical practice approaches and in the second, authors completed the body of the chapter (e.g., introduction, definition, concepts, etc.) then provided a more detailed description of each task linked to supporting evidence. RESULTS: Sixty-seven authors and thirty-five peer reviewers from various backgrounds (physicians, pharmacists, nurses, respiratory therapists) were recruited. On average, there were 32 tasks and 15 situations per chapter. The average number of focus group meetings needed to develop a map (one map per chapter) was 6.7 (SD ± 3.6). We found that the method greatly facilitated integration between different chapters especially for situations which are not limited to a single goal or intervention. For example, almost half of the tasks of the Hypercapnic Ventilatory Failure chapter map were borrowed from other maps with some modifications, which significantly reduced the authors' workload and enhanced content integration. This chapter was also linked to 6 other chapters. CONCLUSIONS: To facilitate curriculum integration, we have developed a knowledge repository consisting of cognitive maps which organize time-sensitive tasks in the proper sequence; the repository serving as the foundation upon which other educational interventions are then built. While this methodology is demanding, authors welcomed the challenge given the scholarly value of their work, thus creating an interprofessional network of educators across Canada.
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 | 0,028 | 0,112 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,011 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,003 |
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 ».