Investigating transformational leadership in action: The case of an effective youth sport coach for athletes with disabilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it