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Record W4406181171 · doi:10.1177/03057356241300538

Please don’t stop the music! A new look at the performance anxiety of musicians with the model of excellencism and perfectionism

2025· article· en· W4406181171 on OpenAlexaff
Patrick Racine, Samuel Vachon Laflamme, Patrick Gaudreau, Frédèric Langlois

Bibliographic record

VenuePsychology of Music · 2025
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsUniversité du Québec à Trois-RivièresUniversity of Ottawa
Fundersnot available
KeywordsPsychologyPerfectionism (psychology)AnxietyPsychoanalysisMusicalDevelopmental psychologyCognitive psychologySocial psychologyVisual artsArtPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.281
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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