Knowledge Transfer: How do High Performance Coaches Access the Knowledge of Sport Scientists?
Bibliographic record
Abstract
The purpose of this research was to answer three specific questions: I) How do coaches perceive sport science research? ii) What sources do coaches consult when looking for new ideas? and iii) What barriers do coaches encounter when trying to access new information? All of the high-performance coaches involved in Canadian Interuniversity Sport (CIS) were contacted to complete an on-line survey related to these questions. There were 205 coaches who completed at least part of the questionnaire. There was a strong consensus that the CIS coaches believe that sport science makes an important contribution to high-performance sport. Gaps exist between what coaches are looking for and the research that is being conducted, especially in the area of tactics and strategies. Coaches are most likely to consult other coaches, or attend coaching conferences to get new information. Sport scientists and their publications were ranked very low by the coaches as a likely source of sport science information. The barriers to the coaches' access to sport science are the time required to find and read scientific journals, and lack of direct access to a sport scientist. Strategies to remove the barriers could include rewarding sport scientists for successful transfer of their knowledge to practice through direct communication with coaches.
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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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".