Coaches’ interpersonal emotion regulation and the coach-athlete relationship
Bibliographic record
Abstract
Researchers have examined the impact of coaches’ emotional expressions and emotional intelligence on athlete outcomes (Allan, V., & Côté, J. (2016). A cross-sectional analysis of coaches’ observed emotion-behavior profiles and adolescent athletes’ self-reported developmental outcomes. Journal of Applied Sport Psychology, 28 , 321–337; Thelwell, R.C., Lane, A.M., Weston, N.J., & Greenlees, I.A. (2008). Examining relationships between emotional intelligence and coaching efficacy. International Journal of Sport and Exercise Psychology, 6 , 224–235; van Kleef, G.A., Cheshin, A., Koning, L.F., & Wolf, S.A. (2018). Emotional games: How coaches’ emotional expressions shape players’ emotions, inferences, and team performance. Psychology of Sport & Exercise ). However, there is little research examining coaches’ use of specific strategies to regulate their athletes’ emotions. The purpose of the present study was to explore the strategies coaches used to try and regulate their athletes’ emotions, and to explore the relationship and contextual factors influencing coaches’ IER strategy use. A longitudinal multiple case study approach was used (Stake, R.E. (2006). Multiple case study analysis. New York: The Guilford Press) with five cases, each consisting of one male coach and two individual varsity sport athletes ( N = 15). Participants completed individual interviews, a two-week audio diary period, and a follow-up interview. Data were inductively and deductively analyzed and a conceptual model was developed outlining athletes’ emotions and emotion regulation, coaches’ IER, the coach-athlete relationship, and contextual factors. Participants described a bidirectional association between the coach-athlete relationship and coaches’ IER. A number of factors influenced athletes’ and coaches’ use of emotion regulation strategies and contributed to the quality of the coach-athlete relationship. The IER strategies that coaches used may reflect instrumental, performance-related motives, and coaches’ IER efforts may also contribute to coaches’ emotional labour.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".