Lessons learned and new strategies for success: Evaluating the Implementation of Competency-Based Medical Education in Queen’s Pediatrics
Bibliographic record
Abstract
Objectives: In 2017, Queen's University launched Competency-Based Medical Education (CBME) across 29 programs simultaneously. Two years post-implementation, we asked key stakeholders (faculty, residents, and program leaders) within the Pediatrics program for their perspectives on and experiences with CBME so far. Methods: Program leadership explicitly described the intended outcomes of implementing CBME. Focus groups and interviews were conducted with all stakeholders to describe the enacted implementation. The intended versus enacted implementations were compared to provide insight into needed adaptations for program improvement. Results: Overall, stakeholders saw value in the concept of CBME. Residents felt they received more specific feedback and monthly Competence Committee (CC) meetings and Academic Advisors were helpful. Conversely, all stakeholders noted the increased expectations had led to a feeling of assessment fatigue. Faculty noted that direct observation and not knowing a resident's previous performance information was challenging. Residents wanted to see faculty initiate assessments and improved transparency around progress and promotion decisions. Discussion: The results provided insight into how well the intended outcomes had been achieved as well as areas for improvement. Proposed adaptations included a need for increased direct observation and exploration of faculty accessing residents' previous performance information. Education was provided on the performance expectations of residents and how progress and promotion decisions are made. As well, "flex blocks" were created to help residents customize their training experience to meet their learning needs. The results of this study can be used to inform and guide implementation and adaptations in other programs and institutions.
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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 | 0.042 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".