Perfectionism and the stress process in intercollegiate athletes: examining the 2×2 model of perfectionism in sport competition.
Bibliographic record
Abstract
This study examined the 2 × 2 model of perfectionism to predict competition-related stress variables in intercollegiate athletes. A sample of 188 athletes (n=99 women) completed measures of sport perfectionism at Time 1 and 4-5 weeks later completed measures of coping, appraisal, affect, and goal progress after a competition. Results of moderated hierarchical regression analysis found support for the 2 × 2 model’s four hypotheses for challenge and control appraisals and goal progress. However, only two hypotheses were supported for threat appraisals, negative affect, and avoidance coping. Exploratory analysis found some evidence that goal progress moderated the relationship between perfectionism, coping, and control appraisals. Overall, the results indicated that pure personal standards perfectionism was associated with better outcomes than pure evaluative concerns perfectionism. For most variables, evaluative concerns perfectionism was related to the poorest outcomes. Overall, the results indicate the 2 × 2 model is a viable framework to evaluate the joint influences of perfectionism dimensions on
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".