Notice bibliographique
Résumé
To the Editor: Across Canada, residency programs are transitioning to competency-based medical education (CBME). In brief, CBME is an outcomes-based educational approach that will change the design, implementation, assessment, and evaluation of residency programs.1 Over the past year, we have provided a resident’s perspective for the redesign of our program’s curriculum as it transitions to CBME. In planning for the transition progresses, a challenging source of discussion has been how to facilitate the culture shift necessary for CBME-based learning and assessment. How do residents dissociate not being deemed competent from feeling a sense of personal failure? Learners and educators already find feedback challenging. Negative feedback is often viewed as failure, even when intended as constructive. In CBME, residents will aim to become “competent” in preestablished milestones. Yet, the expectation is that for most skills, residents will not achieve competency until later in their training. For CBME to succeed, residents must transition from perceiving feedback as a high-stakes process to, instead, viewing it as a continuous and essential part of achieving competency. Learners will have to adjust their expectations, and approach assessments as opportunities for growth rather than notifications of failure. The intention of the CBME framework, which relies on many low-stakes assessments, is that learners will use these assessments as opportunities to identify weaknesses, set learning goals, and then receive the tools needed to facilitate their learning. Educators will have to emphasize that specific feedback is not meant as a global reflection of a learner and be cognizant that feedback be given in a safe environment since “receiving feedback sits at the intersection of these two needs—our drive to learn and our longing for acceptance.”2 The opportunity to be involved in the CBME transition process has spurred us toward becoming intentional about our own learning, thoughtful of knowledge gaps, and inquisitive of feedback—accepting it modestly while being open to our own vulnerability. The transition to CBME is ongoing, and as it progresses, critical self-reflection by learners and educators alike to adapt their mind-sets and maintain a positive learning environment is essential for the success of not only trainees but also residency programs. Alanna Wong, MDSecond-year resident, University of Toronto Medicine, Royal College Emergency Resident Medicine Program, Toronto, Ontario, Canada; ORCID: https://orcid.org/0000-0003-2421-5017. Niran Argintaru, MDFourth-year resident, University of Toronto Medicine, Royal College Emergency Resident Medicine Program, Toronto, Ontario, Canada; [email protected]; Twitter: @EMNiran; ORCID: http://orcid.org/0000-0002-4029-1231. First published online February 13, 2018
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 | 0,006 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,006 | 0,008 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,017 | 0,036 |
| 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 ».