Peer athlete mentoring from the mentor's perspective: A retrospective case study
Bibliographic record
Abstract
To date, researchers have focused solely on proteges' perceptions of peer athlete mentoring relationships (e.g., Hoffmann & Loughead, 2016a, 2016b), while overlooking the perspectives of athletes serving as experienced peer mentors. Using a retrospective qualitative case study design, we examined the experiences of one former exemplary peer athlete mentor (i.e., Nick [pseudonym]). We studied Nick because three current Canadian National team athletes independently named him as their peer athlete mentor in a previous study (i.e., Hoffmann, Loughead, & Bloom, 2017). Following the three-interview series approach (Seidman, 2006), data from three interviews (totaling nearly 5 hours) with Nick were analyzed using thematic narrative analysis. Nick indicated that mentoring was crucial in helping athletes rise to prominence in their sport. He noted that he was motivated to support his proteges for their benefit but also because of the mutually-enhancing aspects of peer mentorship—the latter of which implied he was involved in relational mentoring relationships with his proteges (Ragins, 2016). Nick further described that he had an unwavering belief in his proteges and a deep commitment to them. Lastly, Nick shared his views on the complexity of simultaneously identifying as an elite athlete and a peer mentor. The findings provide new insights into why, and to some extent how, athletes may serve as peer mentors. Given our methodological approach, Nick's perspectives are not necessarily generalizable to others who assume the role of peer athlete mentor. However, the results do suggest that peer mentoring relationships between athletes may be reciprocally beneficial.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".