Athletes’ Perceptions of Developing Relationships Through Adult-Oriented Coaching in Online Contexts
Bibliographic record
Abstract
Online coaching has grown in popularity, in which the coach and athlete work together using Internet-based platforms, without meeting in person. Kettlebell lifting has been using the online format for some time. The majority of Kettlebell lifters are Masters Athletes (MAs), over the age of 35 years, and competing in registered events around the world. Adult-oriented psychosocial coaching approaches that prioritize relationship development have proven to be successful when coaching MAs. While the coach–athlete relationship has been extensively examined, it is not known how the coach–athlete relationship is created and maintained in an online-only environment. The purpose of this study is to explore the perceptions of MAs’ relationships with their online coaches. Five kettlebell lifters were interviewed to explore their experiences of having online coaches. Using interpretative phenomenological analysis, the lifters’ individual experiences within the online coaching environment were examined. Three higher order themes suggest (a) initial relationship building involves the coach selection by the MA, as well as developing closeness and complementary behaviors; (b) progressing in the relationship through communication; and (c) coach programming that is adaptable and negotiated. The coach–athlete relationship for mature adults in an online-only platform can be fostered through adult-oriented approaches.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".