A Chinese perspective on the actual and preferred sources of coaching knowledge
Bibliographic record
Abstract
Efforts to improve coaching effectiveness require an understanding of the common sources of coaches’ knowledge acquisition. Sports coaches utilise multiple learning sources, yet limited direct evidence elucidates the manner in which Chinese coaches learn to coach and the evolution of their learning sources throughout their careers’ development. This research examines the actual and preferred sources of coaching knowledge for Chinese coaches and analyses changes in learning sources from Junior to Senior level coaches. One hundred coaches from China, including 60 Junior coaches, 23 Intermediate coaches and 17 Senior coaches, completed an online questionnaire. The survey results indicated that coaches acquire knowledge from formal, informal and non-formal learning situations. However, formal coach education (coach education programmes) is the most important source of knowledge acquisition for all coaches. Furthermore, as coaches develop, the sources to acquire knowledge will gradually change from athletic experience to interaction with other coaches. Based on these findings, we suggest that national sport governing bodies build more comprehensive coach education systems by establishing a scientific mentoring system and organising regular coach-themed clinics, seminars, meetings and so on. Future research is needed to examine how coaches in China’s dominant programmes learn to coach and how this learning is practically applied.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".