Competency-based faculty development: applying transformations from lessons learned in competency-based medical education
Bibliographic record
Abstract
Faculty development in medical education is often delivered in an ad hoc manner instead of being a deliberately sequenced program matched to data-informed individual needs. In this article, the authors, all with extensive experience in Faculty Development (FD), present a competency-based faculty development (CBFD) framework envisioned to enhance the impact of FD. Steps and principles in the CBFD framework reflect the lessons learned from competency-based medical education (CBME) with its foundational goal to better train physicians to meet societal needs. The authors see CBFD as a similar framework, this one to better train faculty to meet educational needs. CBFD core elements include articulated competencies for the varied educational roles faculty fulfill, deliberately designed curricula structured to build those competencies, and an assessment program and process to support individualized faculty learning and professional growth. The framework incorporates ideas about where and how CBFD should be delivered, the use of coaching to promote reflection and identity formation and the creation of communities of learning. As with CBME, the CBFD framework has included the important considerations of change management, including broad stakeholder engagement, continuous quality improvement and scholarship. The authors have provided examples from the literature as well as challenges and considerations for each step.
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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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".