A description and comparison of youth sport coach leadership behaviours in training and competition
Bibliographic record
Abstract
Research examining the influence of coach leadership behaviours in youth sport is limited (Vella et al., 2013). Accordingly, it is important to generate baseline descriptive data, revealing the extent to which leadership behaviours are currently being employed by youth sport coaches (Potrac et al., 2000). Thus, the purpose of this study was to explore and contrast youth sport coaches' full-range leadership behaviours (i.e., transformational, transactional, laissez-faire and toxic leadership; Bass & Avolio, 1994) across playing contexts (i.e., training and competition). To this end, a systematic observation was employed to examine the frequency of coaches' leadership behaviours. Seven head coaches of youth soccer teams and 46 athletes (Mage = 14.37, SD = 1.32, 28 female) were observed during one training session and one competition for a total of 14 observation sessions. Coaches' leadership behaviours were coded in a continuous, time-based manner for the duration of each recorded session using the Coach Leadership Assessment System (CLAS: Turnnidge & CA´tA©, submitted). A repeated measures ANOVA revealed that coaches most frequently exhibited inspirational motivation, (M = 3.39), intellectual stimulation (M = 1.48), individualized consideration (M = 1.25), transactional (M = 0.99), idealized influence (M = 0.65), laissez-faire (M = 0.11), and toxic (M = 0.06). Additionally, our findings indicate that leadership behaviours, as a whole, were significantly more prevalent in training (M = 2.07) compared to competition (M = 0.12). These findings provide insight into the leadership behaviours employed by coaches across playing contexts. Practical implications and future directions will be 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".