Exploring youth sport coaches’ perceptions of intended outcomes of leadership behaviours
Bibliographic record
Abstract
Youth sport coaches play an important role in shaping the sport experience and positive development of youth athletes. One salient approach to examining how the interpersonal behaviours of coaches influence young athletes has been through the lens of leadership theories. However, there is limited understanding surrounding the underlying processes associated with coach leadership behaviours, such as how and why behaviours are applied. This study sought to examine why do youth sport coaches use specific leadership behaviours and what are their intended outcomes when using these behaviours? Eleven coaches ( M age = 42.3, SD = 14.5) were recruited from competitive youth soccer and volleyball clubs (athletes’ M age = 15.8, SD = 1.9). Coaches participated in a video-stimulated recall interview, during which coaches reflected upon their own coaching behaviours and provided insight into the application of leadership behaviours in youth sport. Findings of the study revealed that contrasting leadership behaviours (e.g. transformational vs. transactional) are associated with distinctive coach objectives (promoting confidence vs. establishing respect). This study can serve to facilitate knowledge mobilization by strengthening our understanding of coaches’ intentions for the implementation of leadership behaviours in a real-world setting.
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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".