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Enregistrement W2966718002 · doi:10.4103/bjhs.bjhs_45_18

Moving toward competency-based medical education

2018· article· en· W2966718002 sur OpenAlexaboutno aff
Tejaswini Vallabha

Notice bibliographique

RevueBLDE University Journal of Health Sciences · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedical educationPsychologyMedicine

Résumé

récupéré en direct d'OpenAlex

Competency-based medical education (CBME) has been adopted by many countries over the last two decades. Medical councils throughout the world are switching over to CBME. Though late, the regulatory body for medical education in India has moved toward CBME. The recent announcement by the Medical Council of India (MCI) regarding the implementation of CBME for UG and PG curricula from the next academic sessions is a major shift in the policy. The concept of CBME started in 1950 when Tyler[1] proposed that the education should be outcome based. Prof. Robert Carroll[2] brought in this concept without using the word competence. Later, an extensive description regarding CBME in comparison with the existing curricular methods was given by McGaghie et al.[3] in 1978. Attempts to correlate course contents, teaching–learning, and outcomes were made by various groups of medical educators randomly. By the end of the 20th century, the need for CBME was crystallized, and the Canadian Medical Council brought out Can Meds Competency Framework in 2004,[4] broadly covering the competencies in six domains to be a medical expert. Similarly, the Accreditation Council for Graduate Medical Education[5] described the expected outcomes in six domains. Over the years, these are refined, modified, and adopted by the medical schools all over the world. McGaghie et al.[3] described how the CBME differs from the subject-centered curriculum and integrated curriculum in three fundamental ways: “Curriculum is organized around functions or competencies required for the practice of medicine in a particular setting It is grounded on empirically validated principle that students of intellectual quality found in the medical schools, when given appropriate instruction can master basic performance objectives It views CBME as an experiment where both processes of learning and technique of learning are considered as hypothesis subject to testing.” The expected outcome would be a physician who can practice medicine at a predetermined level of proficiency as per the needs. The existing curriculum practiced in India is a subject-centric model with attempts of patchy integration. Realizing the limitations and gaps in the outcomes, the MCI has moved to CBME. The major components of CBME are clearly spelled objectives of the program with appropriate teaching–learning methods and methodology of assessment. The curricula of postgraduate degree and diplomas released in March 2018 reflect the efforts toward defining outcomes.[6] The national goals are clearly specific and explicit. Similarly, subject-specific goals and objectives have spelled out the expected outcomes and divided these under various domains. Significant customizations as per the needs of the particular specialization are also attempted within the general regulations and broad framework. As explained in the preamble, the reconciliation team focused mainly on the outcomes and alignment of the rest of the course content compromising grammar and syntax with sole purpose to simplify and reach all the stakeholders for easy implementation.[6] The MCI recently uploaded the curriculum for MBBS.[7] These documents, prepared with significant efforts by the team, have made major changes. The outcomes are clear and explicit with appropriate alignment to core objectives toward producing competent Indian Medical Graduate. It is interesting to note that the document explicitly suggests teaching–learning methods and assessment and addresses vertical and horizontal integration unambiguously. Details of the regulatory component are awaited, which will spell out the necessary information on quantitative implementation-related issues. At this juncture, it is appropriate to recollect and recognize the efforts made by the Rajiv Gandhi University of Health Sciences in the early 2000s, under the leadership of Former Director of Curriculum Development Prof. D. K. Shrinivas. He developed goals, objectives under various domains and suggested T-L methods, guidelines for monitoring progress when majority of universities in India had vague ideas. Implementation of PG curricula is comparatively easier as many universities have similar curricula, but need effective implementation. However, the situation may not be same for the implementation of MBBS curriculum. Although there have been continuous efforts toward sensitizing and training faculty for 4–5 years by the medical education units [MEUs], regional centers, and nodal centers across the country, there is skepticism regarding trickling of concepts at all levels. Learning is probably patchy and more of methodology oriented in general. There will be a challenge for governance to ensure appropriate implementation and effective monitoring of teaching-learning activities and aligning the schedule with existing batches who are receiving a traditional method of training. Grassroots level understanding and coordination remain key methods. Faculty deficit and addressing untrained or reluctant faculty can be a majorhurdle in implementation. The role of enthusiastic, influential, and knowledgeable proactive MEUs in every institute will be invaluable in bringing about change smoothly during this transition period. Ineffective half-hearted implementation can be disastrous, and CBME will be looked upon as an idea with a piecemeal approach. Good supportive governance and provision of needs including adequate faculty will be the cornerstone. Timely assistance for implementation from the MCI is a must. Known for its rigid, nonflexible approach toward accreditation and processes, the MCI should now focus on quality enhancement, rather than blindly following rigid irrelevant assessment methods of medical schools.

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)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,602
Score d'incertitude au seuil0,976

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,030
Tête enseignante GPT0,361
Écart entre enseignants0,332 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
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

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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