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Record W4416817246 · doi:10.1177/10298649251385724

Participant and musical characteristics influence singers’ physiological stress during opera performances

2025· article· en· W4416817246 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMusicae Scientiae · 2025
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMelodyMusicalOperaSituational ethicsStress (linguistics)Range (aeronautics)Categorical variable

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.349
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it