Exploring the correlation between role ambiguity and athlete sport satisfaction
Bibliographic record
Abstract
There has been a recent increase in research examining role ambiguity and athlete satisfaction in sport. Previous studies have found that there is a negative correlation between role ambiguity and athlete satisfaction. The purpose of this study is to explore this correlation between role ambiguity and athlete sport satisfaction, and to examine if gender and need for role clarity act as moderators. This cross-sectional study consisted of 39 competitive athletes recruited from a local high-performance training centre who completed The Athlete Satisfaction Questionnaire, Role Ambiguity Scale and the Need for Role Clarity Questionnaire. Results indicated a significant positive relationship between role clarity and satisfaction. Regression analysis indicated that both gender and need for role clarity were not significant moderators in the relationship between role clarity and athlete satisfaction. Results from this study add to previous literature that the role clarity (or ambiguity) -athlete satisfaction relationship is consistent with different population samples. This research suggests that coaches should consider role clarity/ambiguity to improve group dynamics within their teams, and to work to improve role clarity for their athletes. Moreover, this study provides insight to create interventions to increase athlete satisfaction, which may be related to fewer dropouts and improved performance by 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".