Effect of mindset and self-efficacy on resident feedback seeking beahvior
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".