Making sense of competency-based medical education (CBME) literary conversations: A BEME scoping review: BEME Guide No. 78
Bibliographic record
Abstract
Competency-based medical education (CBME) received increased attention in the early 2000s by educators, clinicians, and policy makers as a way to address concerns about physician preparedness and patient safety in a rapidly changing healthcare environment. Opinions and perspectives around this shift in medical education vary and, to date, a systematic search and synthesis of the literature has yet to be undertaken. The aim of this scoping review is to present a comprehensive map of the literary conversations surrounding CBME. Twelve different databases were searched from database inception up until 29 April 2020. Literary conversations were extracted into the following categories: perceived advantages, perceived disadvantages, challenges/uncertainties/skepticism, and recommendations related to CBME. Of the 5757 identified records, 387 were included in this review. Through thematic analysis, eight themes were identified in the literary conversations about CBME: credibility, application, community influence, learner impact, assessment, educational developments, organizational structures, and societal impacts of CBME. Content analysis supported the development of a heat map that provides a visual illustration of the frequency of these literary conversations over time. This review serves two purposes for the medical education research community. First, this review acts as a comprehensive historical record of the shifting perceptions of CBME as the construct was introduced and adopted by many groups in the medical education global community over time. Second, this review consolidates the many literary conversations about CBME that followed the initial proposal for this approach. These findings can facilitate understanding of CBME for multiple audiences both within and outside of the medical education research community.
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 | 0.025 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.034 | 0.025 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".