Developing Close, Trusting Coach–Athlete Relationships With High-Performance Adolescent Tennis Players
Bibliographic record
Abstract
The purpose of this study was to understand why and how experienced tennis coaches developed quality relationships with their high-performance adolescent athletes that prioritized athletes’ needs and well-being. Five highly regarded Canadian tennis coaches of internationally ranked adolescent players engaged in two semistructured interviews and three story completion tasks. The data were analyzed using reflexive thematic analysis. Findings outlined that coaches unanimously believed establishing a close, trusting relationship with their adolescent athletes was fundamental to creating a caring environment in which empathy for athletes’ athletic, academic, and personal demands could be demonstrated. Coaches also described the difficulties of navigating these close relationships in a climate that is under severe scrutiny because of athlete maltreatment allegations. Examples of coaching behaviors that fostered closeness and maintained athlete safety included demonstrating care towards athletes’ social, emotional, academic, and athletic challenges, encouraging dialogue in which athletes expressed their wants and needs, and involving parents to help maintain transparency regarding the establishment of closeness. Uniquely, this study provides practical suggestions for how coaches can nurture closeness while promoting safe environments that prioritize athletes’ welfare.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".