Examining relations between dimensions of perfectionism and self-compassion in university athletes
Bibliographic record
Abstract
Perfectionism in sport has various dimensions, including evaluative and excessive concerns and criticism, as well as excessively high personal standards (Flett & Hewitt, 2002). Self-compassion (SC) may influence aspects of perfectionism. This study explored relations between perfectionism and SC in 149 female (Mage = 20.0 years, SD = 1.7 years) and 125 male (Mage = 19.7 years, SD = 1.50 years) varsity athletes using the short version of the Multidimensional Perfectionism Scale (MPS, Cox et al., 2002), the Sport MPS (SMPS, Gotwals & Dunn, 2009), and the Self-Compassion Scale (Neff, 2003). Males reported significantly higher scores for the perfectionism variables of personal standards [t(3.29) = 3.29] and perceived parental pressure [t(272)= 5.34], while females reported higher scores for the SC subscale of over-identification [t(272) = -3.84]. SC was significantly related to personal standards (r = -.26), concern over mistakes (r = -.33), perceived coach pressure (r = -.48), organization (r = -.21), and self-oriented perfectionism (r = -.22). The SMPS variables accounted for 27% of the variance in SC. SC was significantly predicted by the composite SMPS variables of personal standards perfectionism (r = -.26, ?= -.15) and evaluative concerns perfectionism (r = -.29, ? = -.22; R2 = .10). The MPS was a weak predictor of SC (R2 = .05). Since lower SC is associated with higher scores on perfectionism dimensions, implications during setbacks should be considered.Acknowledgments: This research was supported by the Social Sciences and Humanities Research Council of Canada.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".