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Enregistrement W4411348721 · doi:10.4300/jgme-d-24-00814.1

Interactive Teaching Strategies for Accreditation Success: Insights From a Canadian Residency Program

2025· article· en· W4411348721 sur OpenAlexafffundabout
Tessa Hanmore, Allie Singers

Notice bibliographique

RevueJournal of Graduate Medical Education · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueReflective Practices in Education
Établissements canadiensQueen's University
Organismes subventionnairesQueen's University
Mots-clésAccreditationMedical educationGraduate medical educationMEDLINEData scienceComputer scienceMedicinePolitical science

Résumé

récupéré en direct d'OpenAlex

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.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,021
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,700
Score d'incertitude au seuil0,987

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,021
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,002
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,039
Tête enseignante GPT0,474
Écart entre enseignants0,435 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission3
Résumé présentoui

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