“Teaching as a Competency”: Competencies for Medical Educators
Bibliographic record
Abstract
Most medical faculty receive little or no training about how to be effective teachers, even when they assume major educational leadership roles. To identify the competencies required of an effective teacher in medical education, the authors developed a comprehensive conceptual model. After conducting a literature search, the authors met at a two-day conference (2006) with 16 medical and nonmedical educators from 10 different U.S. and Canadian organizations and developed an initial draft of the "Teaching as a Competency" conceptual model. Conference participants used the physician competencies (from the Accreditation Council for Graduate Medical Education [ACGME]) and the roles (from the Royal College's Canadian Medical Education Directives for Specialists [CanMEDS]) to define critical skills for medical educators. The authors then refined this initial framework through national/regional conference presentations (2007, 2008), an additional literature review, and expert input. Four core values grounded this framework: learner engagement, learner-centeredness, adaptability, and self-reflection. The authors identified six core competencies, based on the ACGME competencies framework: medical (or content) knowledge; learner- centeredness; interpersonal and communication skills; professionalism and role modeling; practice-based reflection; and systems-based practice. They also included four specialized competencies for educators with additional programmatic roles: program design/implementation, evaluation/scholarship, leadership, and mentorship. The authors then cross-referenced the competencies with educator roles, drawing from CanMEDS, to recognize role-specific skills. The authors have explored their framework's strengths, limitations, and applications, which include targeted faculty development, evaluation, and resource allocation. The Teaching as a Competency framework promotes a culture of effective teaching and learning.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".