The Impact of Program Structure and Goal Setting on Mentors’ Perceptions of Peer Mentorship in Academia
Bibliographic record
Abstract
Many peer mentorship programs in academia train senior students to guide groups of incoming students through the rigors of postsecondary education. The mentorship program’s structure can influence how mentors develop from this experience. Here, we compare how two different peer mentorship programs have shaped mentors’ experiences and development. The curricular peer mentorship program was offered to mentors and mentees as credited academic courses. The non-curricular program was offered as a voluntary student union service to students and peer mentors. Both groups of peer mentors shared similar benefits, with curricular peer mentors (CMs) greatly valuing student interaction, and non-curricular peer mentors (NCMs) greatly valuing leadership development. Lack of autonomy and lack of mentee commitment were cited as the biggest concerns for CMs and NCMs, respectively. Both groups valued goal setting in shaping their mentorship development, but CMs raised concerns about its overemphasis. Implications for optimal structuring of academic mentorship programs are discussed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".