Factors Impacting the Departure Rates of Female and Male Junior Medical School Faculty: Evidence from a Longitudinal Analysis
Bibliographic record
Abstract
BACKGROUND: High rates of attrition have been documented nationally in assistant professor faculty of U.S. medical schools. Our objective was to investigate the association of individual level risk factors, track of academic appointment, and use of institutional leave policies with departure in junior faculty of a research-intensive school of medicine. METHODS: Participants included 901 faculty newly hired as assistant professors from July 1, 1999, through December 30, 2007, at the Perelman School of Medicine at the University of Pennsylvania. The faculty affairs database was used to determine demographics, hiring date, track of appointment, track changes, time to departure, and use of work-life policies for an extension of the probationary period for mandatory review, reduction in duties, and leave of absence. RESULTS: Over one quarter (26.7%) of faculty departed during follow-up. Faculty appointed on the clinician educator or research tracks were at increased risk of departure compared to the tenure track (hazard ratio [HR] 1.87, confidence interval, [CI] 1.28-2.71; HR 4.50, CI 2.91-6.96; respectively). Women appointed on the clinician educator track were at increased risk of departure compared to men (HR 1.46, CI 1.04-2.05). Faculty who took an extension of the probationary period were at decreased risk of departure (HR 0.36, CI 0.25-0.52). CONCLUSIONS: At this institution, junior faculty on the tenure track were least likely to depart before their mandatory review compared to faculty on the clinician educator or research tracks. Female assistant professors on the clinician educator track are of significant risk for departure. Taking advantage of the work-life policy for an extension of the probationary period protects against attrition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".