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Record W4223625716 · doi:10.1017/s0265051722000146

Teacher perspective on music performance anxiety: an exploration of coping strategies used by music teachers

2022· article· en· W4223625716 on OpenAlexaff
Erin MacAfee, Gilles Comeau

Bibliographic record

VenueBritish Journal of Music Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoping (psychology)PsychologyThematic analysisAnxietyMusic educationQualitative researchPianoPerspective (graphical)PedagogyMathematics educationClinical psychologyComputer scienceSociology

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to explore music performance anxiety (MPA) from music teachers’ perspectives by identifying and describing common coping strategies teachers use to support students with MPA. A quantitative content analysis of scientific and non-scientific MPA literature identified preparation, open communication, realistic expectations, exposure therapy and deep breathing as the five most common coping strategies mentioned in the literature. Qualitative thematic analyses of existing literature and interview transcripts from five piano teacher participants provided descriptions of the five commonly identified coping strategies. A comparison of literature and interview results suggests a gap between research knowledge of MPA and practical teaching application. While music teachers employ a variety of strategies to help students cope with MPA, they may also benefit from formal MPA training opportunities grounded in research to provide additional resources for effectively managing students with MPA.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.277
Teacher spread0.199 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations13
Published2022
Admission routes1
Has abstractyes

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