Exploring stakeholder perspectives regarding the implementation of competency-based medical education: a qualitative descriptive study
Bibliographic record
Abstract
Introduction: Competency-based medical education (CBME) offers perceived advantages and benefits for postgraduate medical education (PGME) and the training of competent physicians. The purpose of our study was to gain insights from those involved in implementing CBME in two residency programs to inform ongoing implementation practices. Methods: We conducted a qualitative descriptive study to explore the perspectives of multiple stakeholders involved in the implementation of CBME in two residency programs (the first cohort) to launch the Royal College's Competence by Design model at one Canadian university. Semi-structured interviews were conducted with 17 participants across six stakeholder groups including residents, department chairs, program directors, faculty, medical educators, and program administrators. Data collection and analysis were iterative and reflexive to enhance the authenticity of the results. Results: The participants' perspectives organized around three key themes including: a) contextualizing curriculum and assessment practices with educational goals of CBME, b) coordinating new administrative requirements to support implementation, and c) adaptability toward a competency-based program structure, each with sub-themes. Conclusion: By eliciting the perspectives of different stakeholder groups who experienced the implementation processes, we developed a common understanding regarding facilitators and challenges for program directors, program administrators and educational leaders across PGME. Results from our study contribute to the scholarly conversation regarding the key aspects related to CBME implementation and serve to inform its ongoing development and application in various educational contexts.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".