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Record W13095978 · doi:10.1123/jsep.30.3.383

Self-Presentational Concerns and Competitive Anxiety

2008· article· en· W13095978 on OpenAlexaff
Erin McGowan, Harry Prapavessis, Natascha N. Wesch

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2008
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsWestern University
Fundersnot available
KeywordsPresentational and representational actingPsychologyAnxietyTraitTrait anxietySocial psychologyCognitionClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

The purpose of this study was to improve understanding of the link between self-presentational concerns and competitive anxiety. Specifically, we examined (a) associations among self-presentational concerns and competitive state anxiety dimensional symptom responses using the modified Competitive State Anxiety Inventory-2 (CSAI-2; Martens, Vealey, & Burton, 1990) and (b) whether self-presentational concerns mediate trait-state anxiety relationships. In addressing these matters, we also examined the factor structure and composition of the Self-Presentation in Sport Questionnaire (SPSQ; Wilson & Eklund, 1998). Results showed that self-presentational concerns were positively associated with intensity and frequency dimensional symptoms and negatively associated with direction symptoms. Results also showed that self-presentational concerns demonstrated consistently higher associations with the cognitive component and the intensity symptom of the CSAI-2 state measures. Results showed no support for the notion that self-presentational concerns mediate the trait-state anxiety relationship. When examining the factor structure and composition of the SPSQ, the results from two independent athlete samples support the tenability of an abbreviated 21-item four-factor model. Thus the newly constituted scale is recommended for measuring self-presentational concerns in sport.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.341
Teacher spread0.313 · 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 teacher head, not a consensus.

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

Citations22
Published2008
Admission routes1
Has abstractyes

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