What Makes a Good Coach? Examining the Antecedents of Autonomy-Supportive Behaviors
Bibliographic record
Abstract
Various sport associations employ coaches to shape the environment that children and youth experience. Specifically, a coach’s style of interaction often directly or indirectly influences youth participation and motivation. While research suggests that adopting autonomy-supportive coaching behaviors enhance children and youth well-being and promote overall healthy development, not every coach uses this particular coaching strategy. The present study therefore sought to examine the determinants of coaches’ autonomy-supportive behaviors. The constructs under investigation included ego-involvement, coaching efficacy, perceived athlete competence, and pressure. Data were collected from 100 coaches who currently coach an individual or team sport within the Montreal region. The results demonstrated that motivation efficacy, a sub-factor within coaching efficacy, and perceived athlete competence were positively related to coaches’ autonomy-supportive behaviors. The findings present important implications for practitioners regarding training and development opportunities. In addition, suggestions are provided for managers to superimpose the model on the supervisor-employee relationship.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".