Please don’t stop the music! A new look at the performance anxiety of musicians with the model of excellencism and perfectionism
Bibliographic record
Abstract
Musicians have normalized performance anxiety (PA) to be part of their musical career. Perfectionism has been proposed as a possible personality risk factor for PA. Although perfectionistic concerns have been consistently positively correlated to PA, results have been inconsistent for perfectionistic standards. This inconsistency is potentially attributable to the fact that past studies did not differentiate the pursuit of high standards and the pursuit of perfectionistic standards. In this study, we aimed to compare the levels of PA of students who pursue high standards (excellence) from those who pursue perfection with the model of excellencism and perfectionism. As a supplementary analysis, we have also investigated if different standards predicted different positive and negative affect levels. A total of 94 music students completed questionnaires on perfectionism, PA, and positive and negative affect. They were recruited through their music conservatory ( N = 69) and recruitment ads on Facebook ( N = 25). Results of multiple linear regression demonstrated that only perfectionistic standards were positively and significantly associated with cognitive state anxiety, overall score of PA, and negative affect. Overall, aiming for excellence rather than perfection seemed to help mitigate levels of PA and the negative affect felt by musicians.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".