What Advice Current Pathology Chairs Seek From Former Chairs
Bibliographic record
Abstract
The 2018 Association of Pathology Chairs annual meeting included a panel discussion of Association of Pathology Chairs senior fellows (former chairs of academic departments of pathology who have remained active in Association of Pathology Chairs) about the type of advice that current (sitting) pathology chairs ask them. To inform the panel discussion, information was obtained from the senior fellows by e-mail and subsequent conference call. Of the 33 respondents, 24 (73%) had provided consultation advice (9, <5; 11, 5-10; 2, 10-20; and 2, >20). Most (>75%) of the consultations were provided face-to-face and outside the framework of Association of Pathology Chairs, with 70% of those seeking advice being well known by the consultant(s). Of the senior fellows providing advice, 71% had themselves sought consultation from former pathology chairs and 75% from nonpathology chairs. Modest correlation was found between the number of consultations senior fellows sought when they were chairs and the number of consultations they subsequently provided. The most frequent topics of consultation were strategic planning, balancing the missions, setting department priorities, recruitment of faculty and staff, conflict management, issues specific to new chairs, and resource (money/space) issues. Those who had provided such advice the longest and to the most people indicated that there was no significant change in the type of questions asked over time. Former department chairs can be a valuable source of counseling for current chairs, and organizations of department chairs should consider formalizing the use of these individuals as consultants to sitting chairs.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.012 |
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".