A Core Components Framework for Evaluating Implementation of Competency-Based Medical Education Programs
Bibliographic record
Abstract
PURPOSE: The rapid adoption of competency-based medical education (CBME) provides an unprecedented opportunity to study implementation. Examining "fidelity of implementation"-that is, whether CBME is being implemented as intended-is hampered, however, by the lack of a common framework. This article details the development of such a framework. METHOD: A two-step method was used. First, a perspective indicating how CBME is intended to bring about change was described. Accordingly, core components were identified. Drawing from the literature, the core components were organized into a draft framework. Using a modified Delphi approach, the second step examined consensus amongst an international group of experts in CBME. RESULTS: Two different viewpoints describing how a CBME program can bring about change were found: production and reform. Because the reform model was most consistent with the characterization of CBME as a transformative innovation, this perspective was used to create a draft framework. Following the Delphi process, five core components of CBME curricula were identified: outcome competencies, sequenced progression, tailored learning experiences, competency-focused instruction, and programmatic assessment. With some modification in wording, consensus emerged amongst the panel of international experts. CONCLUSIONS: Typically, implementation evaluation relies on the creation of a specific checklist of practices. Given the ongoing evolution and complexity of CBME, this work, however, focused on identifying core components. Consistent with recent developments in program evaluation, where implementation is described as a developmental trajectory toward fidelity, identifying core components is presented as a fundamental first step toward gaining a more sophisticated understanding of implementation.
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 | 0.160 | 0.217 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.024 | 0.012 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".