The importance of coach-athlete relationships in creating positive university sport experiences
Bibliographic record
Abstract
Although there has been growing interest in the positive development of youth participating in organized sport (Holt, 2016), less attention has been devoted to the impact of university sport on positive development. As in youth sport, coaches continue to play an important role in the development of university level athletes. Following the development of the University Sport Experience Survey (USES; Rathwell & Young, 2016), this study aimed to predict USES subscales from coach-athlete relationships (CART-Q; Jowett & Ntoumanis, 2004) and player demographics (age, sex, year of eligibility, starter/non-starter). A sample of 126 male (46%) and female (54%) university aged athletes (M = 20.3 years, SD = 1.7) from multiple team sports participated in the study. Stepwise multiple regression analyses were used to identify significant predictors for each subscale of the USES. Results show that eight of the nine subscales were predicted by at least one independent variable. For positive subscales of the USES, commitment was the strongest predictor, followed by year of eligibility. The total amount of variance explained in across the positive subscales ranged between 10.9% and 22.3%. For negatives subscales, complementarity was the lone predictor of three subscales while sex predicted the other. Variance accounted for in these models ranged between 3.7% and 17.4%. Results suggest that coaches who wish to promote positive experiences in university athletes should focus on commitment and complementarity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".