New Ideas for High Performance Coaches: A Case Study of Knowledge Transfer in Sport Science
Bibliographic record
Abstract
Research related to how coaches learn concludes that coaches most often learn from other coaches. So far, there has been little evidence to suggest that coaches rely on sport scientists for their information, which would indicate minimal interaction between sport scientists and coaches. The purpose of this study was to determine the type and source of new ideas that high-performance coaches use to understand the extent to which sport science is the source of those ideas. This project utilized a single case study design involving a group of 20 high-performance coaches in 12 different sports in a university environment, which one would expect to be conducive to interaction between sport scientists and coaches. The method included the administration of a questionnaire, followed by a structured personal interview. Our findings suggest that these coaches do believe that sport science can contribute to coaching, are interested in having a sport scientist work with them, and are motivated to find and implement new ideas in their sport programs. Despite this, most of the respondents indicated they usually get those new ideas from other coaches, or from coaching clinics and seminars, and not from sport scientists or their written work. Reasons stated included a lack of time to look for new ideas and hence the use of expedient sources, and a lack of interest in academic publications.
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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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".