Leadership Development for Future Medical School Deans: Outcomes of the AAMC Council of Deans Fellowship Program
Bibliographic record
Abstract
PURPOSE: To determine the outcomes of the Association of American Medical Colleges (AAMC) Council of Deans (COD) Fellowship Program with respect to participants' achieving the goals of becoming a medical school dean and developing leadership skills, and to ascertain fellows' views about the program's value, beneficial aspects, and areas for improvement. METHOD: The 37 COD fellows from 2002 to 2016 were invited to participate in a 2017 survey addressing demographics, training, current leadership position, and value of the program. The survey also included 3 open-ended questions. A 2018 web-based search was conducted to determine fellows' senior leadership roles since their program participation. RESULTS: The survey response rate was 73% (27/37). The majority of respondents were male (82%, 22), aged 51-70 (89%, 25), and white (82%, 22). The top 5 medical specialties reported were internal medicine, pediatrics, anesthesiology, psychiatry, and surgery. Most respondents (63%, 17) reported having a graduate degree. All reported being in leadership positions in academia and/or health care. The web-based search found that 27% (10/37) of the fellows became medical school deans (average tenure 5.6 years); 2 fellows became deans of other types of schools. Overall, survey respondents perceived the program as valuable. Respondents identified shadowing a dean mentor, attending COD meetings, and attending the AAMC Executive Development Seminar for Deans as the most valuable program components. The majority (88%, 23/26) indicated their fellow experience persuaded them to pursue being a dean; 2 (8%) indicated it did not. Respondents identified 4 key opportunities for program improvement: more sponsorship by deans, development of a learning community, enhanced mentoring, and coaching. CONCLUSIONS: The COD Fellowship Program appears to be successful in preparing senior faculty to become deans and assume other senior leadership roles in academia and/or health care. Fellows' feedback will be used to inform future revisions to the program.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".