Pros and Cons: Global Adoption of Competency-Based Medical Education
Notice bibliographique
Résumé
To the Editor: Competency-based medical education (CBME) aims for trainees to develop clinical competencies for effective medical practice rather than focus on knowledge and skill acquisition. Its adoption has grown significantly since the 1970s and has had a global impact on medical education, albeit with varied implementation by country.1,2 CBME was first implemented in developed countries. In the United States, CBME was adopted widely in the early 2000s for residency programs after the Accreditation Council for Graduate Medical Education introduced competency-based education. In the United Kingdom, CBME was included in the General Medical Council’s 2017 postgraduate medical education framework. CBME was implemented for basic and advanced training in Australia by the Royal Australasian College of Physicians in 2016 and is required for specialty training programs in most medical schools in Canada by the Royal College of Physicians and Surgeons. Holland, Denmark, and Switzerland use CBME in medical education in Europe.2 Developing countries have also recognized the benefits of CBME and are implementing it, with India introducing CBME for undergraduate medical education in 2019 and South Africa, Ghana, and Kenya adopting it to address health care worker shortages. Although limited resources and infrastructure hinder its implementation in poorer countries, CBME is gaining popularity in underdeveloped nations.2,3 CBME offers several advantages, such as providing clear and specific learning outcomes for essential competencies in clinical practice and enabling personalized learning at one’s own pace. CBME’s focus on competency-based assessment provides accurate evaluation of a student’s abilities.1 However, CBME has some limitations, such as its complexity, lack of standardization, limited empirical evidence, and potential assessment biases. Addressing these challenges is crucial to maintain CBME’s relevance and effectiveness in medical education.4 Future directions for enhancing the effectiveness of CBME include (1) incorporating advanced technology, like simulation-based training and digital learning tools; (2) emphasizing interprofessional education and collaboration; (3) developing outcome-based curricula that align with essential competencies for clinical practice; and (4) promoting internationalization to improve the recognition of medical qualifications across borders.4 CBME is an innovative approach to medical education, with significant implications for producing competent physicians who can provide high-quality care. Its potential to revolutionize medical education across the globe makes it a promising tool, despite limitations. As medical education evolves, it is essential to consider the benefits and limitations of CBME to ensure its effectiveness and relevance. Rajmohan Seetharaman, MBBS, MD Senior resident, Department of Pharmacology and Therapeutics, Seth Gordhandas Sunderdas Medical College, and King Edward Memorial Hospital, Parel, Mumbai, India; email: [email protected]; ORCID: http://orcid.org/0000-0002-4605-2805
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 | 0,018 | 0,122 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,012 | 0,020 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».