Resident Perceptions of Assessment and Feedback in Competency-Based Medical Education: A Focus Group Study of One Internal Medicine Residency Program
Bibliographic record
Abstract
PURPOSE: As key participants in the assessment dyad, residents must be engaged with the process. However, residents' experiences with competency-based medical education (CBME), and specifically with entrustable professional activity (EPA)-based assessments, have not been well studied. The authors explored junior residents' perceptions regarding the implementation of EPA assessment and feedback initiatives in an internal medicine program. METHOD: From May to November 2018, 5 focus groups were conducted with 28 first-year internal medicine residents from the University of Toronto, exploring their experiences with facilitators and barriers to EPA-based assessments in the first years of the CBME initiative. Residents were exposed to EPA-based feedback tools from early in residency. Themes were identified using constructivist grounded theory to develop a framework to understand the resident perception of EPA assessment and feedback initiatives. RESULTS: Residents' discussions reflected a growth mindset orientation, as they valued the idea of meaningful feedback through multiple low-stakes assessments. However, in practice, feedback seeking was onerous. While the quantity of feedback had increased, the quality had not; some residents felt it had worsened, by reducing it to a form-filling exercise. The assessments were felt to have increased daily workload with consequent disrupted workflow and to have blurred the lines between formative and summative assessment. CONCLUSIONS: Residents embraced the driving principles behind CBME, but their experience suggested that changes are needed for CBME in the study site program to meet its goals. Efforts may be needed to reconcile the tension between assessment and feedback and to effectively embed meaningful feedback into CBME learning environments.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".