Developing Academic Advisors and Competence Committees members: A community approach to developing CBME faculty leaders
Bibliographic record
Abstract
INTRODUCTION: Implementing competency-based medical education (CBME) at the institutional level poses many challenges including having to rapidly enable faculty to be facilitators and champions of a new curriculum which utilizes feedback, coaching, and models of programmatic assessment. This study presents the necessary competencies required for Academic Advisors (AA) and Competence Committee (CC) members, as identified in the literature and as perceived by faculty members at Queen's University. METHODS: This study integrated a review of available literature (n=26) yielding competencies that were reviewed by the authors followed by an external review consisting of CBME experts (n=5). These approved competencies were used in a cross-sectional community consultation survey distributed one year before (n=83) and one year after transitioning to CBME (n=144). FINDINGS: Our newly identified competencies are a useful template for other institutions. Academic Advisor competencies focused on mentoring and coaching, whereas Competence Committee member's competencies focused on integrating assessments and institutional policies. Competency discrepancies between stakeholder groups existing before the transition had disappeared in the post-implementation sample. CONCLUSIONS: We found value in taking an active community-based approach to developing and validating faculty leader competencies sooner rather than later when transitioning to CBME. The evolution of Competence Committees members and Academic Advisors requires the investment of specialized professional development and the sustained engagement of a collaborative community with shared concerns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".