Interactive Teaching Strategies for Accreditation Success: Insights From a Canadian Residency Program
Notice bibliographique
Résumé
In Canada, all postgraduate medical education programs are required to undergo regular accreditation reviews conducted by the Canadian Residency Accreditation Consortium. These reviews can result in accreditation statuses of “Accredited Program” and “Accredited Program on Notice of Intent to Withdraw Accreditation.” Programs placed on Notice of Intent to Withdraw Accreditation must undergo an external review within 24 months, risking the loss of accreditation if they fail to meet standards.This was the situation faced by Queen’s University’s Department of Child and Adolescent Psychiatry residency program, a small program in Kingston with 2 to 3 residents and 11 associated faculty/allied health professions. During their regular accreditation review, the program was placed on notice of intent to withdraw, necessitating thorough preparation for the upcoming external review.The leadership team recognized the need to prepare all stakeholders effectively. Given the small size of the program, it was expected that most stakeholders were already familiar with the relevant information. However, the leadership team wanted to ensure comprehensive preparation without redundancy.Previously, preparation for internal reviews involved lecture-based sessions lasting approximately one hour, led by the Educational Consultant with support from the leadership team. These sessions, while informative, were perceived as repetitive by stakeholders who were already knowledgeable about the content.To address this, the leadership team decided to revise the format of the teaching sessions. They introduced an interactive, question-based approach to present the material. This intervention aimed to refresh known information, introduce new knowledge, and verify stakeholders’ understanding efficiently. Although active learning is not a new pedagogy, in the experience of the leadership team, using it as an engagement strategy for faculty virtual learning has not been widely adopted.The new format involved creating specific questions tailored to each stakeholder group. See the Table for examples of questions.This approach allowed for a more dynamic and engaging learning experience. If stakeholders knew the answers, the session moved on quickly, saving time for areas where knowledge gaps existed. The sessions concluded earlier than planned, demonstrating time efficiency. Additionally, contentious topics were expanded upon regardless of responses to ensure thorough understanding. The time investment for the leadership team in the development and planning of this session was less than creating a standard PowerPoint.Post-session, questions with answer keys and links to source documents were distributed to reinforce learning. Feedback from stakeholders was overwhelmingly positive, highlighting the effectiveness of the format and content. Participants felt better prepared for the external surveyors’ questions and appreciated the opportunity for discussion and active participation.The program performed exceptionally well in the external accreditation review, with all stakeholders demonstrating preparedness and confidence. The leadership team’s innovative teaching strategy not only ensured compliance with accreditation standards but also fostered a collaborative and informed community within the residency program.The Child and Adolescent Psychiatry program at Queen’s University will continue to use this method to present faculty and learners with information. Although this innovation took place at only one program in one institution, the authors believe that with adjustments to the questions it can easily be adapted and used at other institutions and in other specialities.
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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 | 0,011 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,037 | 0,010 |
| Communication savante | 0,010 | 0,003 |
| Science ouverte | 0,005 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».