The "calm, inquisitive" coach versus the "intense, hustle" coach: Implications for athletes' personal development through sport
Bibliographic record
Abstract
Athletes’ intrapersonal regulation of emotions has maintained a dominant focus in the sport-emotion literature; yet, coaches’ emotions may also influence athlete outcomes (Friesen et al., 2013). Thus, the objective of this investigation was to explore the relationship between coaches’ emotions and athletes’ psychosocial development. Male head coaches (N = 9) and their respective female adolescent competitive club soccer athletes (N = 153, Mage = 14.54) were recruited from southeastern Ontario. For each team, coach-athlete interactions were audio and video recorded during two training sessions. Additionally, athletes completed questionnaires assessing the 4 Cs of Positive Youth Development (Vierimaa, Erickson, Côté, & Gilbert, 2012). The Assessment of Coach Emotions (ACE) systematic observation instrument (Allan, Turnnidge, Vierimaa, Davis, & Côté, 2014) was employed for the coding of all video-recorded data. Cluster analyses based on the proportional frequencies of observed emotion-behaviour combinations revealed the presence of two distinct groups: “calm, inquisitive” coaches (n = 6) and “intense, hustle” coaches (n = 3). No significant effects of group membership were found for Confidence, Competence, or Connection to the coach; however, using Pillai’s trace, there was a significant effect of group membership on Character, V = .110, F(2, 131) = 8.097, p < .001. Separate one-way ANOVAs revealed a significant effect of coach group on athletes’ prosocial behaviours, F(1, 132) = 6.067, p = .015, and antisocial behaviours, F(1, 71.193) = 4.985, p = .029. More specifically, athletes of “calm, inquisitive” coaches scored significantly higher on ratings of prosocial behaviours, t(132) = 2.463, p = .015, and lower on ratings of antisocial behaviours, t(71.193) = -2.233, p = .029, than athletes of “intense, hustle” coaches. Despite the comparable success of all coaches in facilitating 3 of the 4 Cs, emotional qualities of coaches’ behaviours appear to have a distinct influence on Character development of young athletes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".