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Record W4386712754 · doi:10.1002/pits.23079

Music performance anxiety in children 9−12 years old in a music program

2023· article· en· W4386712754 on OpenAlexafffund
Catherine Tardif, Hélène Boucher, Julie Lane, Audrey‐Kristel Barbeau

Bibliographic record

VenuePsychology in the Schools · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversité du Québec à MontréalUniversité de SherbrookeUniversité de Montréal
FundersUniversité de Sherbrooke
KeywordsAnxietyPsychologyThe artsMusic educationMusic therapyMedical educationDevelopmental psychologyPedagogyPsychiatryMedicineVisual arts

Abstract

fetched live from OpenAlex

Abstract Music‐intensive school program are one way of enhancing arts‐education in public schools. Elementary school students enrolled in a music‐intensive program are exposed to several stress factors that could contribute to development of music performance anxiety (MPA). This study aims to describe MPA manifestations as reported by 164 students from 9 to 12 years of age taking part in a music‐intensive school program, and to identify what may contribute to its development. Data were collected using a sociodemographic questionnaire, and the French version of the Music Performance Anxiety Inventory for Adolescents. Although it was expected that being involved in an intensive music program would bring students to report higher levels of MPA, results showed moderate levels of MPA. In addition, girls reported significatively more manifestations of MPA than boys. The MPA level also vary according to the origin of the registration in the program (child's request or parents' suggestion), and the cycle. Indeed, cycle 2 students reported significantly less MPA than cycle 3 students when the request to participate in the music‐intensive program came from the child. Findings provide useful key points that should be considered for both practitioners and researchers.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.068
GPT teacher head0.301
Teacher spread0.232 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

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