Beyond bricks and mortar: a rural network approach to preclinical medical education
Notice bibliographique
Résumé
BACKGROUND: Countries with expansive rural regions often experience an unequal distribution of physicians between rural and urban communities. A growing body of evidence suggests that the exposure to positive rural learning experiences has an influence on a physician's choice of practice location. Capitalizing on this observation, many medical schools have developed approaches that integrate rural exposure into their curricula during clerkship. It is postulated that a preclinical rural exposure may also be effective. However, to proceed further in development, accreditation requirements must be considered. In this investigation, academic equivalence between a preclinical rural community based teaching method and the established education model was assessed. METHOD: Two separate preclinical courses from the University of Calgary's three year Undergraduate Medical program were taught at two different rural sites in 2010 (11 students) and 2012 (12 students). The same academic content was delivered in the pilot sites as in the main teaching centre. To ensure consistency of teaching skills, faculty development was provided at each pilot site. Academic equivalence between the rural based learners and a matched cohort at the main University of Calgary site was determined using course examination scores, and the quality of the experience was evaluated through learner feedback. RESULTS: In both pilot courses there was no significant difference between examination scores of the rural distributed learners and the learners at the main University of Calgary site (p > 0.05). Feedback from the participating students demonstrated that the preceptors were very positively rated and, relative to the main site, the small group learning environment appeared to provide strengthened social support. CONCLUSION: These results suggest that community distributed education in pre-clerkship may offer academically equivalent training to existing traditional medical school curricula while also providing learners with positive rural social learning environments. The approach described may offer the potential to increase exposure to rural practice without the cost of constructing additional physical learning sites.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 tête enseignante, 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 ».