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Record W3091009696 · doi:10.1123/jsep.2020-0031

The Adult-Oriented Sport Coaching Survey: An Instrument Designed to Assess Coaching Behaviors Tailored to Adult Athletes

2020· article· en· W3091009696 on OpenAlexaff
Scott Rathwell, Bradley W. Young, Bettina Callary, Derrik Motz, Matt D. Hoffmann, Chelsea Currie

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsCape Breton UniversityUniversity of OttawaUniversity of Lethbridge
Fundersnot available
KeywordsCoachingStructural equation modelingAthletesPsychologyConfirmatory factor analysisApplied psychologyExploratory factor analysisSocial psychologyStatisticsClinical psychologyPsychometricsPhysical therapyMathematicsMedicine

Abstract

fetched live from OpenAlex

Adult sportspersons (Masters athletes, aged 35 years and older) have unique coaching preferences. No existing resources provide coaches with feedback on their craft with Masters athletes. Three studies evaluated an Adult-Oriented Coaching Survey. Study 1 vetted the face validity of 50 survey items with 12 Masters coaches. Results supported the validity of 48 items. In Study 2, 383 Masters coaches completed the survey of 50 items. Confirmatory factor analysis and exploratory structural equation modeling indicated issues with model fit. Post hoc modifications improved fit, resulting in a 22-item, five-factor model. In Study 3, 467 Masters athletes responded to these 22 items reflecting perceptions of their coaches. Confirmatory factor analysis (comparative fit index = .951, standardized root mean square residual = .036, and root mean square error of approximation = .049) and exploratory structural equation modeling (comparative fit index = .977, standardized root mean square residual = .019, and root mean square error of approximation = .041) confirmed the model. The resultant Adult-Oriented Sport Coaching Survey provides a reliable and factorially valid instrument for measuring adult-oriented coaching practices.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.348
Teacher spread0.300 · 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.

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

Citations17
Published2020
Admission routes1
Has abstractyes

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