Relationships between music performance anxiety and impostor phenomenon responses of graduate music performance students
Bibliographic record
Abstract
The purpose of this study was to explore potential relationships between music performance anxiety (MPA) and feelings associated with the impostor phenomenon (IP). Participants completed an online questionnaire comprising demographic questions and two instruments: the Kenny Music Performance Anxiety Inventory (KMPAI) and an adapted version of the Clance Impostor Phenomenon Scale (CIPS-P). Although MPA has been found to affect performers of many ages and levels, IP is associated with individuals who are considered successful. Thus, we chose graduate students pursuing music performance degrees at colleges and universities in the United States and Canada to serve as the participants-individuals who have attained success in music performance. Findings from the 171 participants reveal a strong positive correlation between higher levels of MPA and higher frequency of impostor feelings. Responses indicated that over 75% of participants experienced clinically high levels of MPA and frequent to intense impostor feelings. When asked to comment on their experiences related to the pandemic, some of the performers discussed finding little fulfillment performing without an audience present, others described virtual performances as being less stressful than live, and there was concern that the long period without in-person performances may result in a set-back in dealing with performance anxiety.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".