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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".