Do appraisals mediate the effects of perfectionism dimensions on affective experience during competition among college athletes
Bibliographic record
Abstract
The 2 x 2 model of perfectionism suggests that specific combinations of perfectionism dimensions may differentially influence athlete cognitions and affect. However, appraisal theories hold that emotions and affect are primarily influenced by specific appraisals. This prospective study examined a multiple mediator model in which personal standards perfectionism (PSP), evaluative concerns perfectionism (ECP), and PSPxECP effects on positive (PA) and negative (NA) affect are mediated by threat and challenge appraisals during a competition. Athletes from British Columbia and Quebec (N=187, nfemale=100) completed a measure of perfectionism (SMPS-2), followed 3-4 weeks later by measures of appraisal and affect after a competition. Mediation analysis with bootstrapping (k= 5000; Mplus) revealed that the PSPxECP was the only significant predictor of challenge appraisals (R2= .04). Challenge and PSP had significant effects on PA. Threat and ECP were significant predictors of NA. Examination of specific indirect effects provided marginal support for mediation. Challenge significantly mediated the PSPxECP and PA relationship (unstandardized point estimate (PE) = .042; Bias Corrected Confidence Interval [BCCI] = .014 to .087). PSP had a direct effect on PA (PE = .168, BCCI = .020 to .402). There was no evidence for mediation of NA, with ECP having a significant direct effect (PE = .317, BCCI = .170 to .505). The models predicted R2= .44 in PA and R2= .28 in NA. Overall the data indicates that dimensions of perfectionism have direct effects on affect in competition, with limited evidence that these effects are mediated by threat or challenge appraisals.Acknowledgments: This research was funded by a grant from the Social Sciences and Humanities Research Council.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".