Six ways to get a grip on a mentorship program for residents and faculty
Bibliographic record
Abstract
Mentorship is recognized as beneficial to the personal and professional development of physicians. It has been shown to positively influence career success and research productivity for the mentee, while being associated with increased job satisfaction and lower risk of burnout for the mentor. At an institutional level, when aligned with strategic priorities, mentorship can facilitate gender and racial equality, and improve faculty retention. Consequently, there are calls to prioritize and formalize mentorship, yet the optimal way to achieve this remains elusive. How exactly do we create a mentorship program that is viewed as effective from the perspective of the mentor, mentee, and the institution? In this article we approach mentorship as a complex system, and through this lens we aim to provide medical educators and leaders with guidance on how to create and evaluate a program that provides mentees with distributed and precision mentoring, while also aligning with institutional priorities.
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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 | 0.085 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.011 | 0.026 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".