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Record W3172803828 · doi:10.1177/17479541211021523

How coach leadership is related to the coach-athlete relationship in elite sport

2021· article· en· W3172803828 on OpenAlexaff
Cristina López de Subijana, Luc J. Martin, Javier Maroto Ramos, Jean Côté

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2021
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyTransformational leadershipEliteAthletesQuality (philosophy)Applied psychologyTeamworkSocial psychologyPerceptionSport psychologyManagementPhysical therapyPolitical science

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the association between coach leadership and the coach-athlete relationship. Eighty-one elite athletes ( M = 20.4 years; SD = 3.8; 58% female and 42% male) responded to questionnaires pertaining to their coaches’ leadership behaviours and the quality of their relationship. The overall model for predicting the quality of the coach-athlete relationship according to perceived coach leadership behaviours explained 61% of the variance. Three transformational leadership behaviours were positively associated with the quality of the coach-athlete relationship: individualized consideration, appropriate role-modelling, and fostering acceptance of group goals and teamwork. Based on a gender comparison, men perceived higher levels of leadership pertaining to role-modelling and intellectual stimulation, in addition to higher levels of quality for the coach-athlete relationship. This research emphasizes the importance of engaging in transformational leadership behaviours with regards to associations with perceptions of the coach-athlete relationship in elite sport contexts.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.067
GPT teacher head0.358
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 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

Citations36
Published2021
Admission routes1
Has abstractyes

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