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Record W2066906964 · doi:10.1177/0013164409344520

Athletes’ Perceptions of Coaching Competency Scale II-High School Teams

2009· article· en· W2066906964 on OpenAlexaff
Nicholas D. Myers, Melissa A. Chase, Mark R. Beauchamp, Ben Jackson

Bibliographic record

VenueEducational and Psychological Measurement · 2009
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyCoachingAthletesConfirmatory factor analysisApplied psychologyConstruct validityScale (ratio)Construct (python library)Exploratory factor analysisStructural equation modelingClinical psychologyPsychometricsStatisticsPhysical therapyMathematicsMedicine

Abstract

fetched live from OpenAlex

The purpose of this validity study was to improve measurement of athletes’ evaluations of their head coach’s coaching competency, an important multidimensional construct in models of coaching effectiveness. A revised version of the Coaching Competency Scale (CCS) was developed for athletes of high school teams (APCCS II-HST). Data were collected from athletes ( N = 748) of seven relevant sports. Athlete observations were clustered within teams ( G = 74). Multigroup confirmatory factor analyses of the asymptotic within-teams covariance matrix provided evidence for factorial invariance, except for one residual variance, by athlete gender ( n male = 427, n female = 321). An exploratory multilevel confirmatory factor analysis provided evidence for close fit of an oblique five-factor within-teams structure and a one-factor between-teams structure.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.350
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2009
Admission routes1
Has abstractyes

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