Academic Primer Series: Key Papers About Competency-Based Medical Education
Bibliographic record
Abstract
INTRODUCTION: Competency-based medical education (CBME) presents a paradigm shift in medical training. This outcome-based education movement has triggered substantive changes across the globe. Since this transition is only beginning, many faculty members may not have experience with CBME nor a solid foundation in the grounding literature. We identify and summarize key papers to help faculty members learn more about CBME. METHODS: Based on the online discussions of the 2016-2017 ALiEM Faculty Incubator program, a series of papers on the topic of CBME was developed. Augmenting this list with suggestions by a guest expert and by an open call on Twitter for other important papers, we were able to generate a list of 21 papers in total. Subsequently, we used a modified Delphi study methodology to narrow the list to key papers that describe the importance and significance for educators interested in learning about CBME. To determine the most impactful papers, the mixed junior and senior faculty authorship group used three-round voting methodology based upon the Delphi method. RESULTS: Summaries of the five most highly rated papers on the topic of CBME, as determined by this modified Delphi approach, are presented in this paper. Major themes include a definition of core CBME themes, CBME principles to consider in the design of curricula, a history of the development of the CBME movement, and a rationale for changes to accreditation with CBME. The application of the study findings to junior faculty and faculty developers is discussed. CONCLUSION: We present five key papers on CBME that junior faculty members and faculty experts identified as essential to faculty development. These papers are a mix of foundational and explanatory papers that may provide a basis from which junior faculty members may build upon as they help to implement CBME programs.
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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 | 0.026 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.080 | 0.025 |
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".