Outcomes of Using Peer‐Mentors and Bi‐Directional Development of CanMEDS Core Competencies at a Distributed Medical Education Site
Notice bibliographique
Résumé
INTRODUCTION Due to the growing portfolio of skills needed to succeed as doctors, time and resources allocated by medical schools in Canada to teach anatomy have been greatly reduced and redirected towards other topics. Operating a distributed education campus requires even greater austerity and has the potential to hinder learner acquisition of key medical competencies. Studies in the past have looked at using peer approaches to deal with similar problems in medical education in the anatomy lab. Near peer‐mentoring (NPM), defined as the guidance of less experienced colleagues by colleagues with more experience, was implemented at the distributed education branch of the Schulich School of Medicine & Dentistry in Windsor, Ontario. This paper explores how NPM impacts peer learning and core medical competencies in both learners and teaching assistants (TAs). Some of the CanMED core competencies explored in this study are Medical Expert, Communicators, Collaborators, Leaders, Scholars and Professionals. METHODS Second year medical students volunteered to be TAs for anatomy labs. TAs were responsible for providing feedback on lesson plans, guiding students through lab dissections and answering questions on content. Implementing near‐peer TAs improved teacher to learner ratio to a high degree. Four near‐peer TAs, provided reflections on acquisition of CanMEDs medical competencies through NPM. TA involvement varied depending on the demands of the 1st year curriculum, ranging from 2–8 hours per week during teaching sessions and 1–2 hours of preparation time per lab, per TA. An initial survey was administered to student learners to qualitatively evaluate their current experience in the anatomy lab. A follow‐up survey will be distributed at the end of the final anatomy session. Finally, the faculty anatomist provided a reflection on how the near‐peer mentors helped deliver the anatomy instruction at the distributed education site. RESULTS Twenty‐six learners of thirty‐eight first year learners completed the initial survey. On average, the learners indicated that Medical Expert was their most improved competency during anatomy labs. Learners felt they improved the least on the Leadership competency during anatomy labs. When evaluating TAs, learners ranked the Communicator and Professional competencies the highest. TAs found the near‐peer program to be a meaningful opportunity to integrate and apply multiple competencies at once. Upon reflection, TAs thought that the Medical Expert, Communicator and Leader competencies were most used. In order to improve delivery of anatomy labs in the future, TAs thought more time should be dedicated to understanding the details in delivering the lab session. The near‐peer TA program was felt to improve access to help in learner's critical thinking skills. Further, faculty noted TA development of professional skills including leadership, communication and being collaborators. CONCLUSIONS Teaching Assistants had a positive overall impact on learners experience in anatomy lab. TAs themselves felt near‐peer mentoring to be an emotionally impactful opportunity to practice integrating the CanMed core competencies such as medical expertise, communication and leadership.
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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 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 ».