Examining the Relationship Between Peer Athlete Mentor Leadership behaviours and protégé receipt of mentoring functions
Bibliographic record
Abstract
Mentoring is a process in which a more experienced and knowledgeable individual (the mentor) acts as a role model, provides support and guidance to a developing novice (the protege), and assists in that person’s development (Weaver & Chelladurai, 1999). In organizational settings, research has shown that mentors support their proteges using two types of mentoring functions: vocational and psychosocial (Kram, 1980). Further, research has indicated that mentor transformational and transactional leadership behaviours positively influence protege receipt of these two mentoring functions (Sosik & Godshalk, 2000). There are no known studies investigating the peer-to-peer mentoring that occurs between athletes in sport teams. Thus, the present study examined the relationship between peer athlete mentor leadership behaviours and protege receipt of mentoring functions. Varsity athletes (N = 272) assessed their mentor’s use of transformational and transactional leadership behaviours, and mentoring functions. Using SEM, the results showed the leadership behaviours of inspirational motivation (β = .69, p < .001), democratic behaviour (β = .32, p < .001), social support (β = .29, p < .001), and positive feedback (β = .27, p < .001) were positively related to psychosocial mentoring. Furthermore, the leadership behaviours of intellectual stimulation (β = .51, p < .001), contingent reward (β = .22, p < .005), training and instruction (β = .64, p < .001), and social support (β = .18, p < .001) were positively associated to vocational mentoring. Practical implications of the results are discussed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".