Coach Education and Learning Sources for Coaches of Masters Swimmers
Bibliographic record
Abstract
Masters Athletes (MAs; adult athletes typically over 35 years old who prepare in order to compete at levels ranging from very recreational competition to serious competition) want coaches to cater their approaches to working with adults. Using adult learning principles, we previously found that some coaches cater their approaches in ways to accommodate the manner in which adult athletes prefer to learn. The purpose of this article is to articulate swim coaches’ perceptions of how they learned to work with MAs and whether their formal coach training meets their needs related to coaching MAs. Eleven swim coaches were interviewed regarding how they learned to coach MAs, and were questioned specifically about their coach development broadly and coach education specifically. The data were thematically analyzed and results revealed six main learning sources: coaching experiences (e.g., interacting with MAs, reflection, advice from MAs, coaching youth), experience as an athlete, reading books and Internet searches, networks and mentors, formal coach education, and non-swimming experiences. Results also revealed key themes about coaches’ perceptions regarding coach education, specifically the lack of connection between coach education programs and the Masters sport context, and coaches’ interest in coach education specific to MAs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".