789 Best of both worlds: a hybrid simulation-based junior trainee mock code curriculum with complementary asynchronous e-learning modules
Notice bibliographique
Résumé
Aims Managing pediatric emergencies is a core competency for medical learners, however clinical exposure varies widely during training. Thus, the Hospital for Sick Children established a standardized simulation-based Junior Mock Code (JMC) curriculum (figure 1) for first- and second-year residents. However, trainee and facilitator feedback identified persistent learning gaps, highlighting core pediatric emergencies that were missing from the original curriculum. We aimed to develop a hybrid simulation-based curriculum with complementary interactive asynchronous learning modules (figure 2) to better bridge these gaps. Methods Using the Kern Curriculum Development Model,1,2 we identified common clinical performance weaknesses, knowledge gaps, and low-frequency clinical exposures for junior trainees. We then prioritized the topics to be included in the new curriculum and established its goals and objectives: to provide supplement clinical exposure to core pediatric emergencies, including critical but low-frequency presentations (pediatric trauma and neonatal shock), through simulation-based education and complementary interactive asynchronous learning modules using the AffinityLearning™ platform. We launched our pilot in January 2022. Using Quality Improvement methodology including Plan, Do, Study, Act (PDSA) cycles, we are conducting multiple iterations of feedback collection from learners and facilitators and subsequent curriculum revisions. Results Our first PDSA cycle was gathering stakeholder feedback from a departmental curriculum presentation prior to launch. Pediatric Emergency Medicine faculty and trainees overall responded very positively to the content changes from the previous curriculum and further feedback on logistics, academic potential, and networking opportunities was provided. These logistic suggestions were implemented in designing a curriculum dissemination protocol with administrative support staff. Our second cycle was a peer review of the module content and technological functionality by pediatric emergency medicine faculty and senior trainees, and feedback from this peer review was integrated prior to curriculum launch. Our current cycle started with curriculum implementation and focuses on learner reactions (Kirkpatrick level 1) and learner knowledge retention (Kirkpatrick level 2) outcomes based on feedback surveys for the asynchronous learning modules and aggregate learner performance reports on self-assessment activities embedded in each module. Conclusion Future steps for this curriculum will include assessing trainee performance/competency scores during simulation sessions and correlating these with their performance on the preceding complementary asynchronous e-learning modules (Kirkpatrick level 3 learning outcomes). We will also continuously expand the library of e-learning modules to further bridge learning gaps for junior trainees, empowering them to reflect on their knowledge and areas for improvement as self-directed adult learners. References Thomas PA, et al. Curriculum Development for Medical Education: A Six-Step Approach. Barsuk JH, et al. Developing a Simulation-Based Mastery Learning Curriculum. Kurt S. Kirkpatrick Model: Four Levels of Learning Evaluation.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».