Peer Mentoring for Professional and Personal Growth in Academic Medicine
Bibliographic record
Abstract
Mentorship is a critical component of career development, particularly in academic medicine. Peer mentorship, which does not adhere to traditional hierarchies, is perhaps more accessible for underrepresented groups, including women and minorities. In this article, we review various models of peer mentorship, highlighting their respective advantages and disadvantages. Structured peer mentorship groups exist in different settings, such as those created under the auspices of formal career development programs, part of training grant programs, or through professional societies. Social media has further enabled the establishment of informal peer mentorship through participatory online groups, blogs, and forums that provide platforms for peer-to-peer advice and support. Such groups can evolve rapidly to address changing conditions, as demonstrated by physician listserv and Facebook groups related to the COVID-19 pandemic. Peer mentorship can also be found among colleagues brought together through a common location, interest, or goal, and typically these relationships are informal and fluid. Finally, we highlight here our experience with intentional formation of a small peer mentoring group that provides structure and a safe space for professional and social-emotional growth and support. In order to maximize impact and functionality, this model of peer mentorship requires commitment among peers and a more formalized process than many other peer mentoring models, accounting for group dynamics and the unique needs of members. When done successfully, the depth of these mentoring relationships can produce myriad benefits for individuals with careers in academic medicine including, but not limited to, those from underrepresented backgrounds.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".