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Record W329710997

Experience in competitive youth sport and needs satisfaction: the Singapore Story.

2012· article· en· W329710997 on OpenAlexaff
Koon Teck Koh, John Wang, Karl Erickson, Jean Côté

Bibliographic record

VenueQSpace (Queen's University Library) · 2012
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyCompetitive sportCompetitive athletesSport psychologyApplied psychologyAthletesSocial psychologyPhysical therapyMedicine
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the relationship between sport experiences and psychological needs satisfaction of Singapore high school athletes who were involved in inter-school competition. A total of 1250 school athletes from 22 sports participated in the study. The athletes were between 13 and 18 years old and had an average of 3 years of experience in school sport (SD=.18). Cluster analysis was employed to identify homogenous groups based on the seven developmental experiences domains of the Youth Experience Survey (YES 2.0; Hansen & Larson, 2005). A one-way analysis of variance (ANOVA) was conducted to determine whether differences existed among the clusters in terms of psychological needs satisfaction (i.e., sense of autonomy, perceived competence and relatedness). The results of the cluster analysis showed that there were different subgroups of athletes with distinct developmental experiences, and they varied in the degree to which their psychological needs were satisfied. Generally, subgroups that had high levels of positive experiences and low levels of negative experiences in sport had better fulfillment of psychological needs. It is important to ensure that policies and programmes are formulated, delivered and monitored effectively to promote positive experiences for youth who are involved in competitive sports.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0010.002
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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