Was it all worth it? A graduating resident perspective on CBME
Bibliographic record
Abstract
BACKGROUND: Our institution simultaneously transitioned all postgraduate specialty training programs to competency-based medical education (CBME) curricula. We explored experiences of CBME-trained residents graduating from five-year programs to inform the continued evolution of CBME in Canada. METHODS: We utilized qualitative description to explore residents' experiences and inform continued CBME improvement. Data were collected from fifteen residents from various specialties through focus groups, interviews, and written responses. The data were analyzed inductively, using conventional content analysis. RESULTS: We identified five overarching themes. Three themes provided insight into residents' experiences with CBME, describing discrepancies between the intentions of CBME and how it was enacted, challenges with implementation, and variation in residents' experiences. Two themes - adaptations and recommendations - could inform meaningful refinements for CBME going forward. CONCLUSIONS: Residents graduating from CBME training programs offered a balanced perspective, including criticism and recognition of the potential value of CBME when implemented as intended. Their experiences provide a better understanding of residents' needs within CBME curricula, including greater balance and flexibility within programs of assessment and curricula. Many challenges that residents faced with CBME could be alleviated by greater accountability at program, institutional, and national levels. We conclude with actionable recommendations for addressing residents' needs in CBME.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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; both teacher heads agree on what is shown here.
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".