Curriculum Innovation: The Three Pillars
Notice bibliographique
Résumé
The Michener Institute is Canada’s only postsecondary institution dedicated exclusively to education for the applied health science professions. Michener’s academic innovation strategy is focused on competency-based curriculum design, and leading edge education ensuring Best Experience Best Education for every learner. The compelling reasons for the redesign of health care education include: (1) concerns that the present medical education model is not sufficiently preparing graduates to ensure safe and effective practice, (2) potential compromise to the long-term ability of medical education delivery because of a limited number of clinical education sites, and (3) evidence that highlights the need for rigor in the assessment of competencies for health professions. The Michener Institute has taken a unique approach to address the challenges in medical education. The Academic Innovation Strategy is supported by 3 pillars which include Interprofessional Education (IPE), simulation education, and competency assessment, including assessment of readiness for clinical education. IPE is defined as “occasions when two or more professions learn together with the object of cultivating collaborative practice” (Barr, Freeth, Hammick, Koppel, & Reeves, 2000). New curriculum was developed to ensure graduate competency in interprofessionalism. Micheners’ organizational structure, use of physical resources and organizational communication patterns were redesigned to emulate the foundation principles of IPE. Simulation education provides a safe environment for learners to hone skills in communication critical thinking, crisis management, in addition to the profession-specific technical skills. Simulation provides learners with life situations where there is immediate feedback about decisions and actions in an environment tolerant of errors. By building on established simulation education expertise, Michener was able to reduce dependency on external clinical education sites. Developing authentic assessment of clinical education preparedness is not well documented. Our strategy is to develop authentic assessment to ensure that learners are prepared for clinical practice. Authentic assessment requires real-world application of skills and knowledge that have meaning beyond the assessment activity (Archbald & Newmann, 1988). Several important issues are raised and discussed in relation to the academic innovation strategy. What does an authentic assessment for readiness for clinical include? How much time is required for students to reach clinical competency? How does the curriculum design process support academic innovation? What are the research opportunities? This poster will highlight the rationale for the innovative strategy introduced by Michener, describe the strategic plan and illustrate the leading-edge curriculum design that integrates IPE, simulation education and a readiness for clinical assessment Conflict of Interest: Authors indicated they have nothing to disclose.
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 | 0,048 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,022 |
| Communication savante | 0,030 | 0,021 |
| Science ouverte | 0,003 | 0,027 |
| Intégrité de la recherche | 0,009 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».