Leveraging the power of diagnostic metrics to competency based medical education (CBME) implementation in medical oncology (MO) across Canada.
Notice bibliographique
Résumé
9025 Background: MO training programs across Canada implemented CBME in 2018. Early implementation focused primarily on immediate “structural” changes, and included the adoption of new stages of training, new assessment practices and the creation of Competence Committees. To explore elements that would reflect broader and transformational change related to a true shift to an individualized and competency-based approach to education, program leaders sought to identify, develop, and pilot indicators that could be used by programs to evaluate their implementation of the Competence by Design (CBD) model. It was anticipated that implementation and evaluation of these indicators would be challenging. Methods: In phases one and two of the study, program leaders established a consensus regarding qualities they considered to transformative, and qualitative information regarding how these qualities are reflected in programs was obtained. In phase three, electronic resident portfolios at 2 sample sites were investigated for data regarding specific indicators to determine the feasibility of use by program directors to track implementation progress and aid in program review. Opinions of program leaders in all 14 Canadian programs were obtained through a consensus process. Educators from all sites were invited to participate in semi-structured interviews and a 100% response rate obtained. Data from the 2 sample sites was collected from portfolios, de-identified and reported in aggregate to help maintain confidentiality. Results: 7 key priority indicators were identified. These centered around 6 themes: direct observation, personal learning plans, curricular change, coaching, data sources used by Competency Committees and general concerns about CBD. Variability was found in the extent of implementation of these across programs and in adaptations made locally. At the 2 sample sites, extraction of key metrical indicators from resident portfolios had to be completed manually and was challenging as electronic databases had not been designed to allow easy review and analysis of these specific indicators. Conclusions: Program leaders of Canadian MO training programs were able to reach consensus regarding key data indicators they believe to be transformative and reflective of core CBD principles. Despite this consensus, variability was found in the implementation of these across programs and practical challenges encountered in extracting data related to key indicators from resident portfolios at 2 sample sites. To provide program leaders with data they feel is important for optimal CBD implementation, electronic databases will need ongoing attention and adaptation to facilitate access to key indicators considered important for program review and 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 | 0,042 | 0,123 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 ».