Assessing the Effectiveness of a Transformational Coaching Workshop for Changing Youth Sport Coaches' Behaviours
Bibliographic record
Abstract
Coach development programs should target coaches' interpersonal behaviours (Lefebvre et al., 2016), be informed by behaviour change techniques (Allan et al., 2017), and be subjected to comprehensive evaluations (Evans et al., 2015). As such, informed by the full-range leadership model (Bass & Riggio, 2006) and the Behaviour Change Wheel (Michie et al., 2011), Turnnidge and CA´tA© (2017a) developed the Transformational Coaching Workshop (TCW), which offers training on a range of interpersonal behaviours. The purpose of this study was to evaluate the TCW's impact by observing coaches' behaviours. Participants included 8 male head coaches of youth competitive soccer teams. Systematic observation using the Coach Leadership Assessment System (Turnnidge & CA´tA©, 2016b) was employed pre- and post-workshop to examine coaches' leadership behaviours. Paired samples t-tests, bootstrapped confidence intervals, and effect sizes indicated that idealized influence (p = .067, d = .76, [-114.03, -7.64]), inspirational motivation (p = .087, d = .70, [-265.42, -2.92]), and intellectual stimulation (p = .132, d = .60, [-171.51, -1.25]) behaviours had confidence intervals that did not cross zero and medium to large effect sizes – suggesting that coaches engaged more in these three transformational leadership (TFL) behaviors after the workshop. Furthermore, coaches spent significantly less time displaying neutral (p = .007, d = 1.34) and organizational (p = .001, d = 1.90) behaviours, yet significantly more time displaying leadership through instruction/feedback (p = 0.013, d = 1.17) after the workshop. Overall, this study offers support for providing TFL-theory informed education to youth sport coaches.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".