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Record W4384347209 · doi:10.1123/iscj.2023-0046

Athletes’ Perceptions of Developing Relationships Through Adult-Oriented Coaching in Online Contexts

2023· article· en· W4384347209 on OpenAlexaff
Kimberley Eagles, Bettina Callary

Bibliographic record

VenueInternational Sport Coaching Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsCape Breton University
Fundersnot available
KeywordsCoachingPsychologyPopularityAthletesClosenessPerceptionApplied psychologyPsychosocialThe InternetSocial psychologyComputer sciencePhysical therapyWorld Wide WebPsychotherapist

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.359
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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