Investigating transformational leadership in action: The case of an effective youth sport coach for athletes with disabilities
Bibliographic record
Abstract
There is growing recognition that Transformational Leadership (TFL; Bass & Riggio, 2006) theory may hold significant potential for exploring coaches' influence on athlete development (Vella, Oades, & Crowe, 2013). Although previous research demonstrates that transformational coaching behaviours may have important implications for athlete outcomes (Arthur et al., 2011; Charbonneau et al., 2001), studies examining how coaches apply these behaviours in the youth sport context are limited. The aim of the present work was to investigate an effective youth sport coach's use of transformational coaching behaviours within a program for athletes with disabilities. Semi-structured interviews were conducted with the participant. Interview data were analyzed using an inductive-deductive thematic analysis (Braun & Clarke, 2006), with TFL as the deductive guiding framework. Results revealed eight principles that guided the coach's application of TFL in youth sport: (i) Adopting a person-centred approach, (ii) developing mutually respectful and trusting relationships, (iii) learning from one's mistakes, (iv) believing in one's athletes, (v) recognizing accomplishments, (vi) encouraging athlete input, (vii) adapting to individual needs, and (viii) finding time for fun. Overall, the principles emphasized the creation of caring coach-athlete relationships that fostered athletes' self-determined motivation, confidence, and well-being. These findings provide theoretical insight regarding the application of TFL within the youth sport context and disability sport. Practical recommendations for youth sport coaches who wish to integrate TFL principles into their own coaching practices, as well as potential avenues for future research 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".