Medical Students Learn Professionalism in Near-Peer Led, Discussion-Based Small Groups
Notice bibliographique
Résumé
Problem: Medical educators recognize that professionalism is difficult to teach to students in lecture-based or faculty-led settings. An underused but potentially valuable alternative is to enroll near-peers to teach professionalism. Intervention: We describe a novel near-peer curriculum on professionalism developed at Queen’s University School of Medicine. Senior medical students considered role models by their classmates were nominated to facilitate small-group seminars with junior students on topics in professionalism. Each session was preceded by brief pre-readings or prompts and engaged students in semistructured, open-ended discussion. Three 2-hour sessions have occurred annually. Context: The near-peer sessions are a required component (6 hours; 20%) of the 1st-year professionalism course at Queen’s University (30 hours), which otherwise includes faculty-led seminars, lectures, and online modules. Senior facilitators are selected through a peer nomination process during their 3rd year of medical school. This format was chosen to create a highly regarded position to which students could aspire by demonstrating positive professionalism. Outcome: We performed a qualitative descriptive evaluation of the near-peer curriculum. Fifty-six medical students participated in 11 focus group interviews, which were coded and analyzed for themes inductively and deductively. Quantitative reviews of student feedback forms and a third-party thematic analysis were performed to triangulate results. Medical students preferred the near-peer-led discussion-based curriculum to faculty-led seminars and didactic or online formats. Junior students could describe specific examples of how the curriculum had influenced their behavior in academic, clinical, and personal settings. They cited senior near-peer facilitators as the strongest aspect of the curriculum for their social and cognitive congruence. Senior students who had facilitated sessions regarded the peer teaching experience as formative to their own understanding of professionalism. Lessons Learned: Formal medical curricula on professionalism should emphasize near-peer-led small-group discussion as it fosters a nuanced understanding of professionalism for both early level students and senior students acting as teachers.
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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 | 0,012 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».