Implementing Competency-Based Medical Education in Family Medicine: A Scoping Review on Residency Programs and Family Practices in Canada and the United States
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: While family medicine has been one of the first specialties to implement competency-based medical education (CBME) in residency, the nature and level of its integration with continuing professional development (CPD) is neither well understood nor well studied. The purpose of this review was to examine the current state of CBME implementation in family medicine residency and CPD programs in the North American education literature, with the aim of identifying implementation concepts and strategies that are generalizable to other medical settings to inform the design and implementation of residency training and CPD. METHODS: Using an Arksey and O'Malley six-step framework, we searched five online databases and the gray literature over the period between January 2000 through April 2017. We included full-text articles that focused on the key words CBME, residency, CPD, and family medicine. RESULTS: Of the articles reviewed, 37 met the inclusion criteria and were selected for full review. Eighty six percent of included articles focused on foundation elements related to designing competency-based curriculum and assessment strategies rather than program evaluation or other outcome measures. Only 19% of the articles were related to CPD that focused only on the implementation at the program and/or institution/organization levels. CONCLUSIONS: Given that the implementation of CBME is in its relative infancy, the pattern of implementation activities described in this scoping review reflected a limited focus on a broad range of issues related to fidelity of implementation of this complex intervention.
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 | 0.021 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.028 | 0.038 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".