Real Versus Ideal: Understanding How Coaches Gain Knowledge
Bibliographic record
Abstract
In an ever-evolving society, sport coaches are presented with a number of avenues through which they can acquire and refine their coaching knowledge. The purpose of this research was to replicate and extend past research to gain an up-to-date understanding of how coaches are presently gaining knowledge. This was done through a constructive replication using a sequential explanatory mixed-method design. Study 1 included 798 coaches who completed an online questionnaire detailing their use of 16 sources of coaching knowledge. Coaches’ top three most used sources were interacting with coaches, learning by doing, and observing others. In contrast, the top three most preferred sources were observing others, interacting with coaches, and having a mentor. To contextualize these findings, Study 2 used a qualitative design in which 14 coaches were interviewed to understand their experiences with different knowledge sources. Five distinct narrative types were identified: recent elite athletes, parent coaches, coach developers, teacher coaches, and experienced coaches. Coaches reported engaging in more social and unstructured learning experiences, and the reasons for their preferences appeared to differ based on lifestyle and perceived barriers. Collectively, these findings highlight how coaches gain knowledge and why they prefer certain sources over others.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".