Life After Being a Pathology Department Chair III: Reflections on the “Afterlife”
Bibliographic record
Abstract
The Association of Pathology Chairs Senior Fellows Group provided reflections on activities that have kept them engaged and inspired after stepping down as chair. They offered advice to current chairs who were considering leaving their positions and also to individuals contemplating becoming pathology chairs. A majority (35/41) responded: 60% maintained teaching/mentoring activities; 43% engaged in hobbies; 40% took other administrative positions including deans, medical center chief executive officers, and residency program directors; 31% continued research; 28% wrote books; 20% performed community service; 14% led professional organizations; 14% developed specialized programs; 11% engaged in clinical service; and 11% performed entrepreneurial activities. Most individuals had several of these activities. One-third indicated that those considering becoming chair should be able to place faculty and department needs before their own. One-fourth emphasized the need to know why one wants to become chair, the need to develop clear goals, and the need to know what one wants to accomplish as chair before applying for and accepting the position. More than half (57%) indicated that before stepping down as chair, one should have a clear plan and/or professional goals that can be served by stepping down. Some even suggested that this be in place before applying for the chair. Almost two-thirds (63%) indicated they had no regrets stepping down as chair. These findings may be valuable to those contemplating stepping down from or stepping into any department chair position or other academic leadership role.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".