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Record W4366828237 · doi:10.1080/1091367x.2023.2203137

Invariance Testing of the Adult-Oriented Sport Coaching Survey Across Masters Athletes’ Age, Gender, Competition Level, and Sport

2023· article· en· W4366828237 on OpenAlexafffund
Derrik Motz, Scott Rathwell, Bettina Callary, Bradley W. Young

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2023
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of LethbridgeCape Breton UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCoachingAthletesPsychologyStructural equation modelingMeasurement invarianceSocial psychologyApplied psychologyPhysical therapyMedicineConfirmatory factor analysis

Abstract

fetched live from OpenAlex

The Adult-Oriented Sport Coaching Survey (AOSCS) is a valid and reliable measure of coaches’ and Masters athletes’ perspectives of how often adult-oriented coaching practices are used. However, Masters athletes’ heterogenous traits have been acknowledged as barriers to generalizing research findings on coaching behaviors. Therefore, this study aimed to conduct invariance testing of the AOSCS across groups of Masters athletes based on age, gender, competition level, and sport grouping variables. A sample of 616 Masters athletes (61.9% female, 37.5% male; Mage = 54.47 years, SD = 10.82) completed the AOSCS-A (athlete version) and demographic questions. The results indicated the AOSCS-A demonstrates configural, metric, scalar, and strict invariance across Masters athletes that differed on age, gender, competition level, and sport. This evidence advances the AOSCS-A as an assessment tool by ensuring confidence in the measurement and interpretation of adult-oriented coaching practices reported by Masters athletes, irrespective of age, gender, competition level, and sport.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.141
GPT teacher head0.371
Teacher spread0.230 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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