Incongruence in the coach-athlete relationship: Potential consequences from misunderstanding
Bibliographic record
Abstract
Achieving a shared understanding facilitates positive interpersonal interactions in dyadic relationships (Laing et al., 1966). Based on a case study of a large competitive sport team, we assessed perceptions of the coach-athlete relationships from the perspective of both the coach and each athlete. Each athlete (n = 60) completed the Coach-Athlete Relationship Questionnaire (Jowett & Ntoumanis, 2004) in regard to the head coach of the team (e.g., I feel close to my coach) and a measure of role satisfaction. Meanwhile, the head coach completed a questionnaire designed to assess his meta-perceptions of what each player would report regarding the coach-athlete relationship. We used polynomial regression analysis coupled with response surface methodology to examine how these different perspectives of the coach-athlete relationship relate to athletes' role satisfaction. The regression equation accounted for significant variance in athletes' satisfaction with their role (?R2 = .53, p < .001). The response surface patterns revealed a concave slope along the line of discrepancy (b = -1.08, p = .023) and a positive linear slope along the line of agreement (b = 1.52, p < .001). Put simply, role satisfaction decreased as athletes' perceptions of the coach-athlete relationship began to deviate from the coach's meta-perceptions of the relationships. In addition, role satisfaction was higher when similarity was achieved at a higher absolute level, when compared to similarity at a lower absolute level. In sum, athletes were more satisfied with their role when their perceptions matched the coach's meta-perceptions, and both of them viewed the relationship positively
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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 | 0.039 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".