Residents as Mentors: The Development of Resident Mentorship Milestones
Bibliographic record
Abstract
BACKGROUND: Mentorship of residents by more senior colleagues has been identified as important for stress management and creating an ideal learning environment. OBJECTIVE: We set out to define the attributes of an ideal resident mentor and explore ways to develop these attributes during residency training. METHODS: A 28-member, multi-specialty counsel of residents and fellows used 2 phases of a small group exercise. In the first phase, the group developed desirable attributes of resident mentors and explored means of developing these attributes. In the second phase, the group identified trends in the results, and in a second small group exercise with participants at a major national conference, refined these trends into Resident Mentorship Milestones. RESULTS: The exercises identified 3 common themes: availability, competence, and support of the mentee. We defined milestones for mentorship in each of these areas. CONCLUSIONS: The Resident Mentorship Milestones, developed by a national panel of residents, describe 3 key dimensions of mentorship: availability, defined as making time for mentorship; competence for and success in mentoring; and support of the mentee. These milestones may serve as a novel tool to develop and assess successful resident mentorship models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".