Collegiate Athletes’ Cognitive Beliefs Related to Setback Anxiety and Rumination in Sport
Bibliographic record
Abstract
Setbacks are common occurrences in sport. Recently, setbacks such as injuries and deselection have been accompanied, and confounded, by setbacks related to COVID-19. How students manage a setback may depend on the interaction of two control beliefs: primary control (PC) to directly influence the setback and secondary control (SC) to adjust to it. Purpose: This study investigates the relationships between athletes’ PC and SC and two important sport setback-related outcomes—anxiety and rumination. Method: We employed a cross-sectional design examining collegiate athletes in the USA and Canada (N = 200; Mage = 20.9 years, 143 women, 52 men, 3 non-binary, 2 did not disclose). Using regression tests controlling for setback severity, we examined the interaction effects of athletes’ PC and SC beliefs relative to setbacks, on the anxiety and rumination variables. Results: We found SC beliefs were beneficial to setback-related anxiety and rumination. A significant interaction indicated that particularly when PC was low, SC had negative associations with setback-related anxieties about letting others down (β = –.45, p < .001) and experiencing pain (β = –.37, p < .001). Conclusion: The findings suggest SC beliefs are important for managing setbacks—specifically for attenuating harmful rumination, and specific setback anxieties. We discuss the possibility of enhancing SC beliefs for combatting sport setbacks through control-enhancing interventions as a direction for future research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".