Self-Presentational Concerns and Competitive Anxiety
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".