Unveiling Coach's Perspective: Evidence-Based Recommendations for Designing Coach Development Programmes
Bibliographic record
Abstract
Unfavourable learning environments may negatively impact coaches’ willingness to engage in coach development programmes (CDPs) as well as their ability to achieve intended learning outcomes. However, it is not currently known how coaches’ prefer to engage with CDPs. Thus, the current study aimed to determine the preferred delivery mode and structure of CDPs targeting professional, interpersonal, and intrapersonal knowledge and skills. Coaches (N = 371; 57.7% men; 77.6% white; 61.7% from Ontario-based; 49.3% soccer coaches) rated their preferred delivery mode, module and program duration, and general availability for CDPs targeting professional (i.e., technical and tactical information), interpersonal (i.e., ability to interact with other sport people), and intrapersonal (i.e., understanding oneself) knowledge. Results revealed a preference for in-person delivery of CDPs targeting professional (52.8%) and interpersonal (49.6%) knowledge, but online (asynchronous; 47.2%) for CDPs targeting intrapersonal knowledge. Overall, weekend mornings and afternoons, and weekday nights are when coaches are generally most available for in-person CDPs. Regardless of the type of knowledge, programmes should take no longer than one weekend to complete, with module being
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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 | 0.108 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".