Tuning into musicians’ wellbeing: Research on music performance anxiety (MPA)
Bibliographic record
Abstract
Tuning into musicians' wellbeing: Research on music performance anxiety (MPA) The Music and Mental Health Research Clinic (MMHRC) at the University of Ottawa's Institute of Mental Health Research (IMHR) at The Royal is investigating how to reduce music performance anxiety (MPA) and the benefits of specific coping strategies for musicians. The audience cheers, and smiles are on every face. The music performance was incredible; it emotionally moved the audience, got their toes tapping, and made them forget their worries. On the other hand, the musicians are thinking about the note they missed or their tempo not being quite perfect. The audience does not see the musicians worrying before the performance, the state of anxiety during the performance, or the negative rumination that will occur afterwards. Biologically wired with an innate tendency to default to negative assumptions to avoid dangerous situations, our bodies react to stressors as if our lives are at stake (Baumeister, 2001; Rozin & Royzman, 2001; Ito & Cacioppo, 2005). This negativity bias is a key reason why changing our habits, behaviours, and thought patterns is so challenging. It often results in automatic negative thoughts, such as underestimating available opportunities and resources, and increased sensitivity to perceived threats.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".