“A shoulder to lean on during your first year”—An exploration into a Canadian post-secondary institution’s peer mentor program for varsity student athletes
Bibliographic record
Abstract
The transition period from high school to post-secondary can be particularly challenging for many, including varsity student-athletes (SAs). To better support SAs through this transitional experience, some institutions have created peer mentor programs. What is unclear, however, is the perceived value of these mentorship programs from the perspectives of multiple stakeholder positions. This paper contributes to the Scholarship of Teaching and Learning by presenting findings of a program evaluation that investigated the perceived value of a peer mentor program to its stakeholders. To accomplish this, semi-structured interviews were conducted with 30 participants to discuss SA's experiences with being a first year student, making the transition from high school to post-secondary studies, and also, to discuss their lived experiences with the peer mentor program developed for SAs. Using the findings from the inductive thematic analyses, the peer mentor program's effectiveness, areas of strengths, and areas of improvement are discussed to better align with the stakeholders' needs and experiences. Findings offer insights into a) the trials and tribulations of the first year SA experience, b) how peer mentor programs can better support SA's transition to post-secondary education, c) the benefits of conducting a program evaluation, and d) strategies to enhance the peer mentor program to better support students' needs.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".