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Enregistrement W4379212045 · doi:10.51642/ppmj.v26i3.143

MEDICAL EDUCATION-WHERE DO WE STAND?

2015· article· en· W4379212045 sur OpenAlexaboutno aff
Najam-ul-Hasnain Khan

Notice bibliographique

RevuePakistan Postgraduate Medical Journal · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésComputer sciencePsychology

Résumé

récupéré en direct d'OpenAlex

We were taught in a traditional curriculum.Learner was not an active participant in determining a learning plan.Stress was on Content-Knowledge acquisition.Path of learning was from teacher to student as the content was decided by teacher.Learning was in class rooms and not with reference to actual life situations.It was noncontextual.Teaching was discipline based and student was a passive recipient of knowledge.Typical assessment tool was single subjective measure: viva-voce, Long essay questions or Multiple-choice questions.Assessment tool was in-vitro in artificial conditions as short case, long case.Setting of evaluation was removed from real site of job.No direct observation was made, and no formative feedback was provided.Evaluation was norm referenced.Emphasis was on summative evaluation.There was a fixed time for the components of the curriculum to be learnt.Program evaluation focused on matters of process (e.g., ''Are there objectives for every rotation?''or ''Is there a teacher evaluation form?''). Most learners successfully completed their training by meeting time, process, and curricular requirements.When those requirements were met, the ability to apply what was learned to the actual delivery of patient care was assumed, without assessing whether the application of that learning to health care delivery occurred.When those requirements were met, the ability to apply what was learned to the actual delivery of patient care was assumed, without assessing whether the application of that learning to health care delivery occurred.Now the move is towards Constructivist model where Learner is an active participant in determining a learning plan.Stress is on Outcome-Knowledge acquisition.Educational strategy is Learner centered.Path of learning is Nonhierarchical.Responsibility for content is shared by the student and teacher.Learning is with reference to actual problems faced by professionals and thus contextual.Learning by students is active.Boundaries of disciplines are no more barriers and integrated curricula are being developed.Multiple objective measures for assessment ("evaluation portfolio") are being used.Assessment tool is in vivo.Work place based assessment like Mini clinical examination, Direct observation of procedural skills (DOPS), Case based discussions, and Acute care assessment tool are being utilized.Setting of evaluation is the work place.Direct observation, with formative feedback is in place.Evaluation is criterion referenced.Emphasis is on formative feedback.In contrast, competency-based training is based on the successful demonstration of the application of the specific knowledge, skills, and attitudes that are required for the practice of medicine.In support of Competency Based Medical Education, accreditation requirements have become Increasingly focused on outcomes.For instance, ACGME accredited Internal Medicine programs must now demonstrate evidence of data-driven improvements to the training program by using resident performance data, or outcomes, as a basis for improvement, and use external measures to verify both the learner's and the program's performance (ACGME 2009b).Similarly, all Royal College of Physicians and Surgeons of Canada programs require demonstration of both traditional time-based rotations and specialty-specific competencies (Accreditation Committee 2006).At the level of the individual stakeholder, the transition to a competency-based training model can represent a dramatic redefinition of professional identity.

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)0,005
score de la tête « metaresearch » (Gemma)0,014
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,726
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,014
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0080,000

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 Opus0,025
Tête enseignante GPT0,388
Écart entre enseignants0,363 · 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'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

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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