Building successful student-athlete coach relationships: examining coaching practices and commitment to the coach
Bibliographic record
Abstract
In this study we utilized the concept of commitment to explain the impact of coaching practices on student-athlete's behaviour. We examined the impact of commitment to the coach on the coaching outcome of in-role behaviour, and the influence of coaching practices, of information sharing, training, and encouraging teamwork, on the formation of relationships. We adopted measures from the organizational behaviour literature and surveyed student-athletes at two universities in Canada. The sample included data from 165 student-athletes from two universities. Results from structural equation modeling indicate support for the effect of coaching practices on commitment to the coach. Results also support the effect of commitment to the coach on the student-athletes' role behaviour and performance. By showing that coaching practices impact commitment to the coach, and that commitment to the coach impacts student-athlete role behaviour and performance, the findings have important implications for a better understanding of the determinants of coaches' and athletes' performance. The managerial significance of this research rests in the insight provided into how coaching practices influence athlete's behaviour through commitment to the coach. This study contributes to the literature on coach-athlete relationship within universities and colleges by applying the concept of commitment to the coach. This helps diversity research approaches to understanding coach-athlete relationships and extends prior research on commitment by looking at the context of the relationship between the student-athlete and their coach.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".