Twelve tips for implementing a community of practice for faculty development
Bibliographic record
Abstract
Teaching and learning practices often fail to incorporate new concepts in the ever-evolving field of medical education. Although medical education research provides new insights into curricular development, learners' engagement, assessment methods, professional development, interprofessional education, and so forth, faculty members often struggle to modernize their teaching practices. Communities of practice (CoP) for faculty development offer an effective and sustainable approach for knowledge management and implementation of best practices. A successful CoP creates and shares knowledge in the context of a specific practice toward the development of expertise. CoPs' collaborative nature, based on the co-creation of practical solutions to daily problems, aligns well with the goals of applying best practices in health professions education and training new faculty members. In our article, we share 12 tips for implementing a community of practice for faculty development. The tips were based on a comprehensive literature review and the authors' experiences.
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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 | 0.059 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".