The model of excellencism and perfectionism with athletes: A look at measurement and associations with well-being and performance satisfaction as indicators of thriving
Bibliographic record
Abstract
The Model of Excellencism and Perfectionism (MEP) proposes that the effects of pursuing perfection (i.e., perfectionism) need to be assessed beyond the effects of pursuing excellence (i.e., excellencism). Our objective in this research was to rely on the MEP to further our understanding of athlete perfectionism on two key points. First, we focussed on a scale designed to measure dispositional perfectionism and excellencism – the Scale of Perfectionism and Excellencism (SCOPE) – and tested the factor structure of an adapted version of the SCOPE for athletes (SCOPE-A). Second, we addressed the substantive question of whether perfectionism, beyond excellencism, is beneficial, unneeded, or harmful for athletes’ thriving (i.e., experiencing high levels of well-being and performance satisfaction). We analyzed data collected across four samples of athletes (total N = 1,549). The results of confirmatory factor analyses and multiple regression analyses provided evidence to support the factor structure of scores from the SCOPE-A, and the conclusion that, when it comes to athlete thriving, perfectionism is almost always unneeded and excellencism is almost always good enough. These results shed new light on the connection between excellencism, perfectionism, and athlete thriving, and contribute to future research examining the effects of excellencism and perfectionism in athletes using the SCOPE-A.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".