The Impact of a Junior Faculty Fellowship Award on Academic Advancement and Retention
Bibliographic record
Abstract
PURPOSE: Academic faculty experience barriers to career development and promotion. In 1996, Harvard Medical School (HMS) initiated an intramural junior faculty fellowship to address these obstacles. The authors sought to understand whether receiving a fellowship was associated with more rapid academic promotion and retention. METHOD: Junior faculty fellowship recipients and all other instructor and assistant professors at HMS between 1996 and 2011 were identified. Using propensity score modeling, the authors created a matched comparison group for the fellowship recipients based on educational background, training, academic rank, department, hospital affiliation, and demographics. Time to promotion and time to leaving were assessed by Kaplan-Meier curves. RESULTS: A total of 622 junior faculty received fellowships. Faculty who received fellowships while instructors (n = 480) had shorter times to promotion to assistant professor (P < .0001) and longer retention times (P < .0001) than matched controls. There were no significant differences in time to promotion for assistant professors who received fellowships (n = 142) compared with matched controls, but assistant professor fellowship recipients were significantly more likely to remain longer on the faculty (P = .0005). Women instructors advanced more quickly than matched controls, while male instructors' rates of promotions did not differ. CONCLUSIONS: Fellowships to support junior faculty were associated with shorter times to promotion for instructors and more sustained faculty retention for both instructors and assistant professors. This suggests that relatively small amounts of funding early in faculty careers can play a critical role in supporting academic advancement and retention.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".