A qualitative study of perfectionism among self-identified perfectionists in sport and the performing arts.
Bibliographic record
Abstract
When adopting any measure of perfectionism to examine the characteristic in sport or the 2 performing arts, researchers make assumptions regarding its core features and, sometimes, its 3 effects. So to avoid doing so, in the current study we employed qualitative methods to examine 4 the accounts of self-identified perfectionists. Specifically, the purpose of this study was to 5 explore the opinions and perceptions of high-level, self-identified perfectionists from sport, 6 dance, and music. In particular, we sought to obtain detailed information regarding (i) 7 participants’ perceptions of the main features of being a perfectionist and (ii) how they perceived 8 being a perfectionist to influence their lives. Semi-structured interviews were conducted with 15 9 international/professional athletes, dancers, and musicians. Thematic analysis was used to 10 identify patterns and themes within the transcripts. Three overarching themes were identified: 11 drive, accomplishment, and strain. Being a perfectionist was characterised by the participants as 12 having ever increasing standards, obsessiveness, rigid and dichotomous thinking, and 13 dissatisfaction. The participants also described how being a perfectionist influenced their lives 14 by, on the one hand, providing greater capacity for success in their respective domains but, on 15 the other hand, contributing to varying degrees of personal and interpersonal difficulties. The 16 accounts suggest that, in the main, the content of current models and measures adequately 17 capture the features of being a perfectionist in sport and performing arts. However, a greater 18 focus on obsessiveness, dissatisfaction, and intra- versus inter-personal dimensions of 19 perfectionism would provide further insight into the lives of perfectionists in these domains.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".