Using multiple level modeling to investigate the relationship between coaching attributes and athlete outcomes in youth sport
Bibliographic record
Abstract
There is growing interest in the factors that may influence the potential for youth to gain positive developmental outcomes through their involvement in sport. The current study investigated the relationships between coaching efficacy, athlete perceptions of coaching behavior, and positive youth development (PYD) outcomes of youth sport athletes. Multi level modeling was used to analyze the data as both coaching behavior and PYD were operationalized at the level of the athlete, whereas coaching efficacy was measured at the level of the coach/team. Data was collected from 26 teams and 194 athletes who completed the Youth Experiences Scale (YES) as a measure of PYD, as well as the Coaching Behavior Scale for Sport (CBS-S), as an individual-level assessment of coaching behavior. The26 coaches completed the Coaching Efficacy Scale (CES). Statistically significant models emerged for four of the seven YES variables - teamwork, basic skills, negative experiences, and positive relations. In each model, athlete assessments of coaching behavior were significant predictors, however, the coach level variable of coaching efficacy was only a significant predictor for the outcome of negative experiences. Thus, it appears that the primary significant predictors of positive development outcomes are the individuals' perceptions of the participants as opposed to the team level variable of coaching efficacy. Acknowledgments: Funded by SSHRC
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".