Resident perceptions of Competency-Based Medical Education
Bibliographic record
Abstract
BACKGROUND: Residency training programs in Canada are undergoing a mandated transition to competency-based medical education (CBME). There is limited literature regarding resident perspectives on CBME. As upper year residents act as mentors and assessors for incoming cohorts, and are themselves key stakeholders in this educational transition, it is important to understand how they view CBME. We examined how residents who are not currently enrolled in a competency-based program view that method of training, and what they perceive as potential advantages, disadvantages, and considerations regarding its implementation. METHODS: Sixteen residents volunteered to participate in individual semi-structured interviews, with questions focussing on participants' knowledge of CBME and its implementation. We used a grounded theory approach to develop explanations of how residents perceive CBME. RESULTS: Residents anticipated improved assessment and feedback, earlier identification of residents experiencing difficulties in training, and greater flexibility to pursue self-identified educational needs. Disadvantages included logistical issues surrounding CBME implementation, ability of attending physicians to deliver CBME-appropriate feedback, and the possibility of assessment fatigue. Clear, detailed communication and channels for resident feedback were key considerations regarding implementation. CONCLUSIONS: Resident views align with educational experts regarding the practical challenges of implementation. Expectations of improved assessment and feedback highlight the need for both residents and attending physicians to be equipped in these domains. Consequently, faculty development and clear communication will be crucial aspects of successful transitioning to CBME.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".