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Record W2557773045 · doi:10.1177/2167479516679412

Team Member Communication and Perceived Cohesion in Youth Soccer

2016· article· en· W2557773045 on OpenAlexaff
Colin D. McLaren, Kevin S. Spink

Bibliographic record

VenueCommunication & Sport · 2016
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCohesion (chemistry)Optimal distinctiveness theoryGroup cohesivenessPsychologySocial psychologyPerceptionTeam sportAthletes

Abstract

fetched live from OpenAlex

Although it is assumed that athletes need to consider the member-to-member interactions that take place within a team before drawing an accurate perception about the team’s level of cohesion, little research to date has addressed this assumption. The purpose of this study was to examine the intrateam communication and cohesion relationship to determine which types of communication would be associated with perceived task and social cohesiveness in a sample of youth athletes. Youth soccer players ( N = 139, k = 13) completed measures of intrateam communication and task and social cohesion halfway through a competitive season. Separate multilevel analyses were run predicting task and social cohesion. For task cohesion, acceptance, positive conflict, and negative conflict communication emerged as significant predictors, p < .001, accounting for 40% of the total variance. For social cohesion, distinctiveness, positive conflict, and negative conflict communication were significant predictors, p < .001, accounting for 27% of the total variance. Findings provide initial evidence establishing a link between intrateam communication and cohesion in the youth sport context but more importantly suggest both similarities and differences with respect to the specific types of intrateam communication that are associated with task and social cohesion.

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)0.006
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.048
GPT teacher head0.319
Teacher spread0.271 · 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

Citations28
Published2016
Admission routes1
Has abstractyes

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