Implementing Competency-Based Medical Education in Family Medicine: A Narrative Review of Current Trends in Assessment
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The implementation of effective competency-based medical education (CBME) relies on building a coherent and integrated system of assessment across the continuum of training to practice. As such, the developmental progression of competencies must be assessed at all stages of the learning process, including continuing professional development (CPD). Yet, much of the recent discussion revolves mostly around residency programs. The purpose of this review is to synthesize the findings of studies spanning the last 2 decades that examined competency-based assessment methods used in family medicine residency and CPD, and to identify gaps in their current practices. METHODS: We adopted a modified form of narrative review and searched five online databases and the gray literature for articles published between 2000 and 2020. Data analysis involved mixed methods including quantitative frequency analysis and qualitative thematic analysis. RESULTS: Thirty-seven studies met inclusion criteria. Fourteen were formal evaluation studies that focused on the outcome and impact evaluation of assessment methods. Articles that focused on formative assessment were prevalent. The most common levels of educational outcomes were performance and competence. There were few studies on CBME assessment among practicing family physicians. Thematic analysis of the literature identified several challenges the family medicine educational community faces with CBME assessment. CONCLUSIONS: We recommend that those involved in health education systematically evaluate and publish their CBME activities, including assessment-related content and evaluations. The highlighted themes may offer insights into ways in which current CBME assessment practices might be improved to align with efforts to improve health care.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".