A comparison of well-peer mentored and non-peer mentored athletes’ perceptions of satisfaction
Bibliographic record
Abstract
The purpose of the present study was to compare well-peer mentored and non-peer mentored athletes' perceptions of satisfaction. A total of 444 intercollegiate athletes (272 well-peer mentored and 172 non-peer mentored) from a variety of sport teams participated in the study. Athletes from both well-peer mentored and non-peer mentored groups reported their satisfaction levels using the Athlete Satisfaction Questionnaire. The results of a MANOVA and follow-up post hoc ANOVAs showed that well-peer mentored athletes were significantly more satisfied than their non-peer mentored counterparts in terms of individual performance, personal dedication, team task contribution, team social contribution, team integration, ethics, ability utilisation and training and instruction. Overall, the findings suggest that athletes who are well-peer mentored by a teammate perceive higher satisfaction levels with various aspects of their athletic experience than athletes who are not peer mentored by a teammate. Given these positive findings, practitioners (i.e., coaches, sport psychology consultants) should inform athletes on the benefits of peer-to-peer mentoring. The practical implications of the results and strategies to promote peer athlete mentoring relationships in sport are highlighted.
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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 | 0.004 |
| 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 | 0.000 |
| 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".