Exploring Perceptions of Competency-Based Medical Education in Undergraduate Medical Students and Faculty: A Program Evaluation
Bibliographic record
Abstract
Background: There is limited work exploring competency-based medical education (CBME) in undergraduate medical education. We aimed to assess medical students' and faculty's perception of CBME in the undergraduate medicine setting after its implementation at our institution through a Content, Input, Process, Product (CIPP) program evaluation model. Methods: We explored the rationale for the transition to a CBME curriculum (Content), the changes to the curriculum and the teams involved in the transition (Input), medical students' and faculty's perception of the current CBME curriculum (Process), and benefits and challenges of implementing undergraduate CBME (Product). A cross-sectional online survey was delivered over 8-weeks in October 2021 to medical students and faculty as part of the Process and Product evaluation. Results: Medical students displayed greater optimism towards CBME, compared to faculty, in terms of its role in medical education (p<0.05). Faculty were less certain about how CBME was currently implemented (p<0.05), as well as how feedback to students should be delivered (p<0.05). Students and faculty agreed on perceived benefits to CBME implementation. Faculty time commitment to teaching and logistical concerns were reported as perceived challenges. Conclusion: Education leaders must prioritize faculty engagement and continued professional development of faculty to facilitate the transition. This program evaluation identified strategies to aid the transition to CBME in the undergraduate setting.
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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 | 0.037 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".