Impact of a formal mentoring program on academic promotion of Department of Medicine faculty: A comparative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it