Youth sport coaches' reflections on leadership behaviors during games and practices
Bibliographic record
Abstract
Youth sport coaches shape the developmental sporting experience for their athletes (Camiré, Trudel, & Forneris, 2014). Specifically, coaches who form individualized, supportive relationships with their athletes can increase the development of personal and social skills (Fraser-Thomas, Côté, & Deakin, 2005). In light of the value of these relationships, increasing evidence is prompting the application of leadership theories, such as Transformational Leadership (TFL), in youth sport (Vella et al., 2013). The aim of this study was to explore coach perceptions of how and why leadership behaviours are applied in the youth sport context. Eleven coaches (Mage= 42.3, SD= 15.2) were recruited from competitive youth soccer and volleyball clubs (athletes' Mage= 15.8, SD= 1.9) in Eastern Ontario and participated in a stimulated recall interview. During the interviews, coaches reflected upon their own coaching behaviours and provided insight into the application of leadership behaviours in youth sport. Responses were prompted by relevant video sequences from recorded practice and game sessions. A thematic content analysis revealed that; i) coaches use a variety of leadership behaviours in youth sport, ii) the use of leadership behaviours vary across sport contexts or settings, and iii) contrasting leadership styles (e.g., transactional vs. transformational) are associated with distinctive coach objectives (e.g., promoting confidence vs. demanding respect). These findings have helped identify gaps within coach education, and provide theoretical insight for applying leadership theories, and more specifically TFL, to help improve the sport experiences of young athletes.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".