Impact of a formal mentoring program on academic promotion of Department of Medicine faculty: A comparative study
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of a formal mentoring program on time to academic promotion and differences in gender-based outcomes. METHODS: Comparisons of time to promotion (i) before and after implementation of a formal mentoring program and (ii) between mentored and non-mentored faculty matched for covariates. Using paired-samples t-testing and mixed repeated measures ANCOVA, we explored the effect of mentor assignment and influence of gender on time to promotion. RESULTS: Promotional data from 1988 to 2010 for 382 faculty members appointed before 2003 were compared with 229 faculty members appointed in 2003 or later. Faculty appointed in 2003 or later were promoted 1.2 years (mean) sooner versus those appointed before 2003 (3.7 [SD = 1.7] vs. 2.5 [SD = 2], p < 0.0001). Regardless of year of appointment, mentor assignment appears to be significantly associated with a reduction in time to promotion versus non-mentored (3.4 [SD = 2.4] vs. 4.4 [SD = 2.6], p = 0.011). Gender effects were statistically insignificant. Post hoc analyses of time to promotion suggested that observed differences are not attributable to temporal effects, but rather assignment to a mentor. CONCLUSIONS: Mentoring was a powerful predictor of promotion, regardless of the year of appointment and likely benefited both genders equally. University resource allocation in support of mentoring appears to accelerate faculty advancement.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".