Closing the Loop: Exploring Student-Mentors’ Dual Roles in a Longitudinal Group Mentorship Course
Bibliographic record
Abstract
Purpose: Peer mentorship has emerged as an important strategy for supporting medical students, demonstrating multiple benefits for mentees and mentors. However, to our knowledge, no studies have focused on medical students' simultaneous experiences as students (receiving mentorship) and near-peer mentors (providing mentorship). This study explored students' experiences as students and mentors in a longitudinal, group mentorship course. Method: The authors used interpretive description to explore how senior medical students acting as near-peer mentors understood their dual roles. In-depth semi-structured interviews were conducted between April and October 2023. Additionally, students were asked to record audio diaries. Using an iterative, reflexive approach, the authors conducted a constant comparative data analysis. Results: 20 students completed 2 interviews and audio diaries. Four themes were identified: Participants understood their dual roles to be complementary, highlighting a continuity between being students and near-peer mentors, which they called "closing the loop." Many participants cited their experiences in the course as the reason they decided to become student mentors, expressing their desire to "pay it forward." Mentorship roles reminded them of their experiences as new medical students, and "gazing backwards" helped boost their confidence and renew their empathy. Several participants drew attention to how their mentorship role helped them "imagine the future," reaffirming their desire to continue mentoring or teaching. Discussion: Being in two roles was perceived as a positive experience. Dual roles helped boost students' confidence and empathy while fostering meaningful reflection on career trajectories, findings that could help inform or enhance peer mentorship programs.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".