Effect of mindset and self-efficacy on resident feedback seeking beahvior
Notice bibliographique
Résumé
Purpose: In the competency based medical education framework adopted by the Royal College of Physicians and Surgeons of Canada, graduating residents are required to demonstrate proficiency in a set of competencies deemed essential for patient care. Feedback is a crucial component in medical residency training and is an integral part of the learning process. It is not known what drives resident learners to seek feedback or not. Published literature suggests that mindset (individuals’ views of their intelligence) and self-efficacy (individuals’ optimism for perform a task successfully) play a role in feedback seeking in medical education. The purpose of this study is to better elucidate the relationship between mindset/self-efficacy and residents’ feedback seeking behavior in the context of competency based medical education.Setting: Junior surgical residents (PGY1 and PGY2) at McGill University from Dec 2019- February 2020 were surveyed. Interviews were held between July 2020- August 2020.Participants: Junior surgical residents (n= 61) from the Surgical Foundations residency training program comprising 9 surgical subspecialties.Design: A mixed methods study design was utilized. Quantitative data collection involved assessing mindset and self-efficacy using a questionnaire and collating the number of completed Entrustable Professional Activity forms (unit of measure for feedback seeking). We calculated the relationship between mindset, self-efficacy and Entrustable Professional Activities (EPAs) using correlation models. Qualitative data collection involved semi- structured interviews with the residents, exploring experiences in seeking and receiving feedback. Audio recordings were transcribed and analyzed to identify themes. Results: 30 residents participated in the study and completed the questionnaires. More than half of the residents in both PGY1(~55%) and PGY2 (~ 60%) groups held growth mindsets and the remainder held mixed mindsets. Correlational analysis showed a strong positive correlation between mindset and self-efficacy (PGY1 = rs 0.58, P 0.0085; PGY2 = rs 0.74, P 0.0091). There was also a strong association between mindset and the number of completed EPAs (PGY1 = rs 0.63, P 0.0041; PGY2 = rs 0.70, P 0.016). Qualitative analysis resulted in 5 emerging themes: Types of feedback, timing, relationship with staff, learning environment, and feedback cost/benefit. Growth mindset residents appreciated all kinds of feedback, valued challenges and were always focused on devising strategies to overcome difficulties. Mixed mindset residents were more focused on avoiding challenges in the fear of appearing incompetent.Conclusion: Growth mindset appears to be a significant predictor of resident feedback seeking behavior. Instilling a growth mindset early on in the residency training may positively impact their learning and feedback seeking behavior. Raising awareness regarding growth mindset amongst attendings may also help to strengthen the teaching relationship and create an effective learning environment for 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 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 | 0,002 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».