Conceptualization of Competency-Based Medical Education Terminology in Family Medicine Postgraduate Medical Education and Continuing Professional Development: A Scoping Review
Bibliographic record
Abstract
PURPOSE: To examine the extent, range, and nature of how competency-based medical education (CBME) implementation terminology is used (i.e., the conceptualization of CBME-related terms) within the family medicine postgraduate medical education (PGME) and continuing professional development (CPD) literature. METHOD: This scoping review's methodology was based on Arksey and O'Malley's framework and subsequent recommendations by Tricco and colleagues. The authors searched 5 databases and the gray literature for U.S. and Canadian publications between January 2000 and April 2017. Full-text English-language articles on CBME implementation that focused exclusively on family medicine PGME and/or CPD programs were eligible for inclusion. A standardized data extraction form was used to collect article demographic data and coding concepts data. Data analysis used mixed methods, including quantitative frequency analysis and qualitative thematic analysis. RESULTS: Of 470 unique articles identified, 80 (17%) met the inclusion criteria and were selected for inclusion in the review. Only 12 (15%) of the 80 articles provided a referenced definition of the coding concepts (i.e., referred to an article/organization as the definition's source), resulting in 19 highly variable-and 12 unique- referenced definitions of key terms used in CBME implementation (competence, competency, competency-based medical education). Thematic analysis of the referenced definitions identified 15 dominant themes, among which the most common were (1) a multidimensional and dynamic concept that encompasses a variety of skill components and (2) being able to use communication, knowledge, technical skills, clinical reasoning, judgment, emotions, attitudes, personal values, and reflection in practice. CONCLUSIONS: The construction and dissemination of shared definitions is essential to CBME's successful implementation. The low number of referenced definitions and lack of consensus on such definitions suggest more attention needs to be paid to conceptual rigor. The authors recommend those involved in family medicine education work with colleagues across medical specialties to develop a common taxonomy.
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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 | 0.086 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.036 | 0.036 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".