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Record W4407133273 · doi:10.1123/ijsc.2024-0231

Coaching With Social Media: The Coach–Athlete Performance Team

2025· article· en· W4407133273 on OpenAlexaff
Elyse Gorrell

Bibliographic record

VenueInternational Journal of Sport Communication · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsBrock University
Fundersnot available
KeywordsCoachingPsychologyAthletesApplied psychologySocial mediaPhysical therapyComputer sciencePsychotherapistMedicine

Abstract

fetched live from OpenAlex

The purpose of this research was to explore coaches’ perceptions of athletes’ social media use. There has not been a thorough examination of social media’s effects on coaches or the consequences of social media for meaningful relations between coach and athlete. This is an important area to investigate because previous literature suggests that social media has psychological ramifications that influence athletes’ behavior; that athletes may not be aware of, or understand, the implications that social media may have; and that athletes do not realize that social media platforms have an effect on their mental game in sport performance. Given that the coach–athlete relationship is important to sport-performance success, it is important to examine coaches’ perspectives on social media’s influence on athletes. Accordingly, a phenomenological approach was utilized to understand this experience. Semistructured interviews were conducted with six high-performance coaches of a variety of individual sports. The interviews underwent a phenomenological-analysis sequence to coconstruct meaning from the data and explore the topic. Although there is limited research on the topic of social media’s impact on coaches, research has recognized that athletes’ preoccupation with social media is a perceived challenge for coaches. The findings from the present study illustrate how coaches observe social media influencing their athletes’ behaviors, the persuasive qualities of social media, and how young athletes navigate their image on social media.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.317
Teacher spread0.291 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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