If you build it, will they come? Assessing coaches’ perceptions of a sport psychology website
Bibliographic record
Abstract
Sport psychology has become widely applied in coaching practices to assist in athletic success. Despite the increased use, there is a lack of evidence-based sport psychology information available to coaches in the form of informal sources. The aim of this study was to conduct a pilot test to explore coaches’ perceptions and use of an evidence-based sport psychology website. Eight coaches participated in semi-structured interviews. Based on engagement, five of the coaches’ data was analyzed using thematic analysis on their experiences and revealed two main themes, website use and website enhancements. The remaining three coaches’ data was analyzed for their reasons for having limited engagement with the website and used to help identify ways it could be enhanced. In general, coaches perceived the website to be a reliable source that offered practical information (e.g. worksheets) that could be used directly with athletes, and was perceived as easy to navigate and to access information. There were, importantly, various improvements suggested for the website including delivery modality and guides for navigating the order for reading the information. This study offers evidence on the value of online resources in providing coaches with informal and evidence-based sport psychology learning opportunities, and also addresses a number of barriers to engagement as insight for website developers.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".