How is a champion made? – Insights from nine team China Olympic and world champion trampolinists
Bibliographic record
Abstract
This study was conceived to identify key factors contributing to the success of China's trampoline Olympic and world champions, specifically exploring how China's cultural context and unique sports system support their sustained success. Semi-structured interviews with nine Olympic and world champion trampolinists were analyzed using reflexive thematic analysis within a Confucian relationist framework, highlighting the relational nature of knowledge and interactions among psychological traits, training, and social contexts. Three interrelated themes were developed. (1) Social Support: Coaches provided technical guidance and psychological encouragement. Teammates fostered a balance of collaboration and competition. Families offered emotional support and stability. These elements underpinned a comprehensive social support system. (2) Behavioral Coping Strategies: Athletes relied on clear goal setting, scientific and reflective training, and adaptability in competitions to overcome challenges. (3) Psychological Adjustment: Emotional regulation, focus-oriented self-regulation and psychological conditioning were central to coping with the demands of high-pressure competition. These elements are interwoven, collectively shaping the athletes' developmental journey and emphasizing the need for an integrated training ecosystem to sustain championship performance.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".