Recruiting Faculty Leaders at U.S. Medical Schools: A Process Without Improvement?
Bibliographic record
Abstract
Recruiting faculty leaders to work in colleges of medicine is a ubiquitous, time-consuming, costly activity. Little quantitative information is available about contemporary leadership recruiting processes and outcomes. In this article, the authors examine current recruiting methods and outcomes in colleges of medicine and compare academic search approaches with the approaches often employed in intellectual-capital-rich industries.In 2015, the authors surveyed chairs of internal medicine at U.S. medical schools regarding their recruiting practices and outcomes-specifically their selection methods, the duration of searches, the recruitment of women and minorities underrepresented in medicine (URM), and their satisfaction with search outcomes.The authors found that department chairs were extensively engaged in numerous searches for leaders. The recruitment process most commonly required 7 to 12 months from initiation to signed contract. Interestingly, longer searches (19+ months) were much more frequently associated with a recruitment outcome that chairs viewed as unsatisfactory or very unsatisfactory. Most leadership searches produced very few women and URM finalists. The biggest perceived hurdles to successful recruitment were the need to relocate the candidate and family and the shortage of good candidates.The process of recruiting leaders in academic medicine has changed little in more than 25 years. Process improvement is important and should entail carefully structured search processes, including both an overhaul of search committees and further emphasis on leadership development within the college of medicine. The authors propose specific steps to enhance recruitment of members of URM groups and women to leadership positions in academic medicine.
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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 | 0.313 | 0.403 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".