It’s not a virus! Reconceptualizing and de-pathologizing music performance anxiety
Bibliographic record
Abstract
Music Performance Anxiety (MPA) is one of the most widespread and debilitating challenges facing musicians, affecting significant numbers of performers in terms of both their personal and professional functioning. Although numerous interventions exist to target MPA, its prevalence remains unchanged since the first large-scale studies of the 1980s, indicating that available interventions are having limited impact. This review synthesizes and critiques existing literature in order to investigate possible reasons for the limited efficacy of current approaches to managing MPA. Key concepts discussed include conceptual and methodological challenges surrounding defining MPA, theoretical perspectives on MPA's etiology and manifestation, and the coping strategies and interventions used to manage MPA. MPA has predominantly been investigated pathologically and defined as a negative construct manifesting in unwanted symptoms. Based on this conceptualization, interventions largely seek to manage MPA through ameliorating symptoms. This review discusses possible reasons why this approach has broadly not proved successful, including the issue of relaxation being both unrealistic and counterproductive for peak performance, issues associated with intentionally changing one's state creating resistance thus exacerbating anxiety, and focusing on the presence of, rather than response to, symptoms. Despite 50 years of research, MPA remains an unsolved enigma and continues to adversely impact musicians both on and off the stage. Reconceptualizing MPA as a normal and adaptive response to the pressures of performance may offer a new perspective on it, in terms of its definition, assessment and management, with practical as well as theoretical implications.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".