Career transitions: Reflections of former chairs and academic health center leaders
Bibliographic record
Abstract
The 2022 Association of Pathology Chairs Annual Meeting included a live discussion session and a pre-meeting recorded panel webinar sponsored by the Senior Fellows Group (former chairs of academic departments of pathology who have remained active in the Association of Pathology Chairs). The presentation was focused on transition planning for academic health center leaders. Each of the discussion group panelists had served as a pathology department chair as well as in more senior leadership positions, and they provided perspectives based upon their personal experiences. It was noted that such positions are often "at will" appointments of indeterminate length and that those above department chair generally carry greater risks and less stability. Becoming "addicted" to a leadership position was not considered beneficial to the individual or to the institution served and makes transitioning more difficult. Ongoing organizational succession planning was deemed helpful to mitigate such addiction and facilitate personal transition planning. Modes of transitioning discussed included those planned (e.g., voluntary retirement, resignation, administrative advancement) and unplanned (e.g., being "fired"; unexpected personal, health, or family issues). Unplanned transitions were felt to be more difficult, while anticipating when it is time to go and planning for it provided greater personal fulfillment after transition. Many career options were identified after serving in a leadership position, including a return to teaching, research, and/or clinical service; writing; mentoring; becoming more active in professional organizations and boards; philanthropic work; and "reinventing oneself" by moving to another career entirely.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".