The Impact on Peer Mentorship After Implementation of a Competency-Based Residency Curriculum in Canadian Radiation Oncology Training Programs
Notice bibliographique
Résumé
Purpose: Peer mentorship provides professional and personal support between physicians with similar experiences and levels of training. While peer mentorship has shown to benefit academic success and professional growth, little data has examined contextual factors, such as curricular change, that may affect the quality of these relationships. This study aims to explore the impact of a new, nationwide radiation oncology (RO) residency curriculum, known as competence by design (CBD), on peer mentorship experiences between Canadian RO residents. Methods and Materials: A qualitative study, with a social constructivist approach, was conducted with 2 groups of Canadian RO residents. The first were those in the academic year before CBD implementation (non-CBD cohort), and the second were those in the inaugural year of CBD (CBD cohort). Semistructured 1-on-1 interviews were conducted to explore experiences of peer mentorship as it related to curriculum change. Interviews were transcribed and analyzed with deductive and inductive methods until data saturation. Results: Between April and December 2021, 14 participants (6 non-CBD and 8 CBD residents) from 8 out of 10 eligible English-speaking RO training programs across Canada participated. Three major themes were identified: (1) the CBD cohort identified fewer opportunities for peer mentorship, with specific concerns regarding new evaluation processes and uncertainty about the later stages of training; (2) there was minimal impact on specialty-specific learning; and (3) peer mentorship thrived when occurring as spontaneous in-person interactions. Conclusions: Inaugural residents of a CBD curriculum perceived fewer opportunities for peer mentorship. There were specific concerns about new evaluative processes, though this did not affect specialty-specific learning. Peer mentorship was most impactful as informal and in-person interactions. Our findings suggest that unintended consequences of curriculum change may be mitigated by improving communication about new training objectives and increasing opportunities for informal interactions between residents.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».