Learning communities in medical education: A scoping review protocol
Notice bibliographique
Résumé
Medical schools are enriching programs that form the foundation for students’ lifelong careers as physicians. However, along with all of the positive outcomes and experiences part of medical school, many students report feelings of burnout, stress and/or depression. For example, a 2006 systematic review found that United States and Canadian medical students’ levels of psychological distress were consistently higher than the age-matched general population, and that this trend continued throughout each year of training1. Online and hybrid learning have also further contributed to these sentiments along with isolation and a lack of connectedness. A study conducted in nine medical schools in the state of Florida used questionnaires to evaluate the top stressors of medical students and their effects 2. These included medical school peer relations and conflicts in work-life balance and relationships, which caused effects ranging from poor academic performance, decreases in empathy, suicidal ideation, or leaving medical school. Therefore, further studying and finding creative ways to improve the overall well-being and academic success of medical students is of keen interest. A method in which this has been done is through establishing learning communities in medical schools. In broad terms, learning communities involve small group activities between students and selected faculty mentors, with an emphasis on community, collaboration, and professional development. Learning communities vary in their implementation, size, importance, and individual components throughout different medical schools. Components of learning communities may include but are not limited to social activities, small group reflections, having a dedicated physical location on campus for each small group, or integrating learning communities and clinical skills teaching together. There have been studies conducted centered around different learning communities currently present throughout medical schools, as well as their composition and comprehensive effects on students. The goal of this scoping review is to analyze overarching trends from the literature to piece vital aspects together. Emphasis will be placed on finding core aspects and protocols of learning communities that proved to be most important to medical students and or faculty and their experience. Accomplishing this will hopefully enable medical schools to create learning communities that effectively enhance medical education and lifelong skills, values, and attitudes needed for strong careers as physicians.
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 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 | 0,076 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,006 |
| Méta-épidémiologie (sens large) | 0,016 | 0,014 |
| Bibliométrie | 0,023 | 0,016 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,086 | 0,012 |
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 ».