Leadership Trends in Academic Pediatric Departments
Bibliographic record
Abstract
OBJECTIVE: To examine recent turnover trends among chairs of academic pediatric departments. METHODS: Membership data for the 150 institutions represented by the Association of Medical School Pediatric Department Chairs in the United States, Canada, and Puerto Rico were reviewed for the time period of 1993-2003. RESULTS: From 1993 to 2003, 278 individuals (250 men and 28 women) held the position of chair. The mean time of service was 5.58 +/- 3.2 years (median: 5 years). Twenty-nine individuals served continuously as chairs during the 11-year period. Seventy-two individuals served as interim chairs. Twenty-eight women were appointed either chairs or interim chairs during the 11 years. The number of female chairs decreased from 13 in 1993 to 11 in 2003. Female chairs were in office 3.42 +/- 2.72 years. A total of 123 departments had a change in leadership, with a mean annual turnover rate of 17% (range: 4.6-24%). Three departments had 5 different leaders as either interim chair or chair and 6 departments had 4 different leaders during this time period. Neonatology was the most common subspecialty represented by recent pediatric chairs, although nephrology was the subspecialty with the greatest proportional representation. CONCLUSIONS: Departments of pediatrics have high turnover of leadership. Women, in particular, serve for relatively short periods and appear to be under-represented within the leadership of pediatrics. Efforts should be made to ascertain personal qualities that allow sustained leadership and to attract more women into leadership positions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".