Continuing Professional Development for Faculty: An Elephant in the House of Academic Medicine or the Key to Future Success?
Bibliographic record
Abstract
The scope of change required by academic medical centers (AMCs) to maintain their viability and achieve their tripartite mission in the future is large; such reform is affected by numerous global, national, and local forces. Most AMCs focus their transformational efforts on organizational infrastructure (e.g., undertaking payment reform, developing new organizational structures, investing in information technology) and educational programs (with subsequent changes in undergraduate and graduate medical education curricula). Although useful, these efforts have failed to produce the kind of change required for AMCs to succeed in the future.The authors of this Invited Commentary describe a key element missing from most of these reform efforts-the preparation of faculty for new models of health care and educational practice. To address this issue, they call for the effective, system-aligned presence of continuing professional development (CPD) programs. CPD combines continuing medical education, with its focus on content knowledge, and faculty development, with its focus on evidence-based learning methodologies, across the institution to produce a more robust, system- and outcomes-oriented program to facilitate both individual and organizational learning. If sufficiently supported, CPD programs can provide a platform for the human changes necessary to ensure the smooth transition of AMCs to new models of education, clinical research, and ultimately patient care.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.043 | 0.039 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".