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Record W2156876520 · doi:10.5539/jel.v2n1p240

Coaches’ Coaching Competence in Relation to Athletes’ Perceived Progress in Elite Sport

2013· article· en· W2156876520 on OpenAlexvenueno aff
Frode Moen, Roger André Federici

Bibliographic record

VenueJournal of Education and Learning · 2013
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingCompetence (human resources)PsychologyEliteElite athletesAthletesPerceptionApplied psychologySocial psychologyPhysical therapyPolitical sciencePoliticsMedicine

Abstract

fetched live from OpenAlex

This article looks at whether higher levels of perceived coaching competencies focusing on relational issues,were associated with higher satisfaction among elite athletes with their progress in sport. In order to explore this,we investigated elite athletes’ perceptions of their coaches’ coaching competence (CCS) and how theseperceptions related to their own satisfaction with their progress in sport during the last year. The CCS measurescore competencies for coaches as defined by the coaching profession (Moen & Federici, 2011). Our hypothesiswas partly confirmed as the results revealed that higher perceived coach competencies were associated withhigher athlete satisfaction with their progress in sport. This result applies for all the five dimensions of the CCS.However, the group of athletes who are most dissatisfied with their progress in sport do not follow this trend, asthey in general score higher on the different dimensions of the CCS compared to the nearby levels.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.316
Teacher spread0.296 · 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

Citations31
Published2013
Admission routes1
Has abstractyes

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