Participant and musical characteristics influence singers’ physiological stress during opera performances
Bibliographic record
Abstract
Some music performance situations are more stressful than others for performers. Through comparison of heart rate or heart rate variability during different categorical levels of difficulty, researchers have begun to understand the situational factors impacting stress. However, to date, there has been no systematic investigation of how musical difficulty (“musical factors”) may impact performers’ physiological stress. We addressed this gap in the literature by analyzing n = 356 excerpts of cardiac activity from 22 opera trainees performing in four different opera productions. We next modeled cardiac activity as a function of an ensemble parameter (i.e., whether the singer performed solo, in an ensemble, or in a chorus), and the musical characteristics of melodic range and tempo. Although participant-related characteristics had the largest influence on the variability of cardiac activity, Bayesian regression modeling showed small but systematic effects of melodic range and tempo on cardiac activity which depended on whether the excerpt was performed solo or with others. These results suggest that musical factors do impact stress and should be considered alongside situational factors impacting stress.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".