Elite tennis player's perceptions of coaching effectiveness
Bibliographic record
Abstract
The purpose of this study was to explore the experiences of six elite Chinese tennis players and their perspectives of coaching effectiveness within their sport. We collected data using two approaches: semi-structured interviews and player documents (e.g., detailed biographies, competition experience, and performance statistics). Data were analyzed using a reflexive thematic analysis to identify the coaches’ knowledge and behaviors that elite Chinese players considered to be effective and ineffective within the training and competitive landscape. We constructed two overarching themes to represent elite tennis player's perceptions regarding coaching effectiveness: (1) perceived coaching roles (i.e., the coach as a planner, teacher, and strategist), and (2) coaching as a social phenomenon (i.e., interpersonal styles and networks, effective and ineffective behaviors). Theoretically, this study aligns with the 3 + 1Cs model of the coach–athlete relationship and enriches the literature on coaching effectiveness in elite tennis. Practically, this study underscores the value of coaching knowledge and behaviors in shaping player's competitive performance and sociopsychological abilities and identifies recommendations for coaches and players to develop harmonious and meaningful relationships within the elite tennis context.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".