Music performance anxiety in choral singers
Bibliographic record
Abstract
Music Performance Anxiety (MPA) can inhibit a singer from performing to the best of her ability; however, choral conductors are in a position to be able to help singers under their leadership cope with MPA. A Music Performance Anxiety Survey was administered to 85 community choral singers, ages 14-75, in a small musical community in British Columbia, Canada. It was a mixed-methods study investigating how singers experience MPA and how conductors can help singers cope with MPA. Quantitative data were collected concerning singers’ physical and psychological symptoms of MPA, and the factors that influence their experience of MPA. Qualitative data were collected regarding singers’ opinions of how conductors could best help them. Results indicated that 95% of the participants experienced some degree of anxiety-related symptoms prior to performing and that anxiety levels were higher prior to performing than during performance. It was found that psychological symptoms, such as fear, were more bothersome than physical symptoms such as being unable to relax. Memorizing the repertoire was the factor that had the greatest influence on levels of MPA. The development of trusting relationships emerged as the most effective way that conductors can help singers achieve more satisfying performances. Recommendations for physical strategies, behavioural approaches, and addressing the psychological symptoms of MPA are given.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".