Multidimensional perfectionism and sport performance: a systematic review and meta-analysis
Bibliographic record
Abstract
Evidence regarding the perfectionism-performance relationship in sport is inconsistent, leading to an ongoing debate about whether perfectionism helps or hinders athletes in achieving their best performance. To address this, we provide a systematic review and meta-analysis of research examining the relationship between multidimensional perfectionism and sport performance. A literature search returned 31 studies with 46 samples (N = 6,102). A systematic review of this literature suggests that research varies methodologically, with mixed findings for perfectionistic strivings (PS) and perfectionistic concerns (PC). The meta-analysis found that perfectionistic strivings were positively related to sport performance (r+ = .21; CI = .15, .26), while perfectionistic concerns were unrelated (r+ = .03; CI = –.02, .08). Total unique effect revealed that, overall, perfectionism was positively associated with sport performance (TUE = .17; CI = .13, .22), with perfectionistic strivings being primarily responsible for the effect. Moderation analyses showed that the relationship between perfectionistic strivings and sport performance was stronger in older athletes. Evidence for a perfectionistic tipping point was also found showing that PS only predicts better performance when PC is low. We suggest that the interplay between PS and PC is key to understanding this relationship further.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".