Can perfectionism subtypes predict stress variables and achievement goal progress in Canadian university athletes? A prospective test of the 2 x 2 model of dispositional perfectionism
Bibliographic record
Abstract
Based on the 2 x 2 model of dispositional perfectionism (Gaudreau & Thompson, 2010), evaluative concerns and personal standards dimensions of perfectionism coexists in each person. Specific outcomes can be examined in four distinct perfectionism subtypes: pure self-oriented (SOP), mixed, non, and pure socially prescribed (SPP). CIS athletes (N=189; 48% male) completed the Sport-MPS-2 (Gotwals & Dunn, 2009), with perfectionism dimensions examined by the personal standards and concerns over mistakes scales. After a competition 4-5 weeks later, athletes completed measures of cognitive appraisal, coping function, positive (PA) and negative affect (NA), and goal progress. Results of hierarchical regressions showed that pure SOP was associated with higher levels of control and challenged appraisals, goal progress, and PA compared to non-perfectionism. These two subtypes did not significantly differ on threat appraisals, avoidance coping, and NA. Pure SOP was associated with higher levels of control and challenge, and goal progress and lower threat appraisals, avoidance coping, and NA compared to mixed perfectionism. Mixed perfectionism was associated with higher levels of control and challenge, goal progress, and PA compared to pure SPP. These two subtypes did not significantly differ on threat, avoidance coping, and NA. Overall, results show that pure SOP is generally related with better outcomes whereas pure SPP, and to a lesser extend mixed perfectionism, are associated with worst outcomes.Acknowledgments: Funded by SSHRC
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".