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Enregistrement W2069790584 · doi:10.1111/j.1365-2923.2008.03184.x

Enriching the clerkship curriculum with blended e‐learning

2008· article· en· W2069790584 sur OpenAlexaffabout
Adam Szulewski, Lindsay Davidson

Notice bibliographique

RevueMedical Education · 2008
Typearticle
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensQueen's University
Organismes subventionnairesnon disponible
Mots-clésCurriculumMedical educationSession (web analytics)Clinical clerkshipSpecialtyContext (archaeology)Blended learningTeaching methodMedicineComputer sciencePsychologyMathematics educationPedagogyEducational technologyFamily medicineWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Context and setting The pre-clinical years of undergraduate medical education have recently witnessed a shift away from the traditional, lecture-based model of teaching towards new instructional methods designed to encourage student-centred learning. Blended e-learning, which combines online self-study modules with face-to-face teaching, can be used to achieve this goal. Although the use of blended e-learning in the pre-clinical years has been described in the literature, there have been few reports about this method of instruction at the clinical clerkship level. Why the idea was necessary Prior to this project, formal clinical clerkship teaching at our university involved didactic weekly seminars presented by faculty members. Rising enrolment without any increase in the number of teachers led us to investigate novel methods of content delivery. The purpose of this project, therefore, was to improve teaching for clinical clerks at our institution while maximising the quality of the time spent with teaching faculty. What was done A web-based module was created to complement the material covered in the existing clinical clerkship seminar ‘Acute Hand Injuries’. The objectives for the module were developed with reference to national specialty society objectives as well as those published by the Medical Council of Canada. The module was presented as a series of authentic clinical cases and included self-assessment questions with embedded feedback. Given the solid theoretical foundation provided by completion of the new module, the existing 60-minute didactic seminar was modified into a 30-minute session that focused on the consolidation of knowledge through a review of relevant cases. The effectiveness of this blended e-learning model was compared with that of our traditional approach. One group of clinical clerks attended the traditional, expert-led seminar and another completed the new case-based, online module and then attended the shorter session with the same expert, who facilitated discussion and answered questions. Both groups then completed a content-based quiz and participated in focus groups designed to better understand the students’ reactions to their learning experience. Evaluation of results and impact Although the number of students was too small for statistical significance, the group that completed the online module scored 1.15 marks (out of 10) more on the content-based quiz than the group that attended the didactic seminar. During the focus group sessions, it became clear that students who had completed the module and subsequent follow-up session felt more engaged during the seminar and were more confident about applying their knowledge to cases. Other benefits included consistency of instruction and flexibility of use. It was concluded that this blended e-learning approach should be implemented in clerkship teaching as it enriches the student learning experience by increasing student engagement and encouraging active learning. At present, three modules have been completed and are in use; two others are in development. We anticipate that blended e-learning with the use of online modules will become a central part of our clinical clerkship curriculum. These resources will be shared with other institutions by means of submission to digital repositories.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0030,002
Science ouverte0,0010,004
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,003

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,310
Écart entre enseignants0,300 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

Citations11
Publié2008
Routes d'admission2
Résumé présentoui

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