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Record W7062822837

Windows to wellbeing: Insights from music performance science

2021· article· en· W7062822837 on OpenAlexaboutno aff

Bibliographic record

VenueMinerva Access (University of Melbourne) · 2021
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBiopsychosocial modelPsychological interventionSituatedAnxietyMental health
DOInot available

Abstract

fetched live from OpenAlex

The emotional life of performers is complex. To perform with freedom, spontaneity, and creativity, they must be prepared to take risks and ‘feel the fear’, but they must also find ways to manage their fear so they can be physically and mentally capable of expressing themselves freely and creatively. A nuanced approach is needed to help performers navigate this territory. Applying interventions to enhance performance requires us to be cognisant to the performer’s stage of development and performance ambitions. These are situated within a myriad of biopsychosocial factors and educational and occupational demands that collectively influence musicians’ health across their lifespan. In this talk I draw from clinical, research and teaching practice to discuss windows to psychological wellbeing - tried and tested approaches to performance anxiety management. My explanation explores basic psychological needs, self-regulated learning principles, performance routines for emotional regulation, and psychological flexibility. Strategies will be suggested for musicians to implement in their performance practice. Reference: Osborne, M.S. (2021, 27-30 October). Windows to wellbeing: Insights from music performance science. Keynote presented at the International Symposium of Performance Science on “Performance Health and Wellbeing”, Montréal, Canada.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.003
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0060.013
Scholarly communication0.0080.007
Open science0.0010.007
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.206
Teacher spread0.186 · 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

Labeled directly by 2 models reading the full record.

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

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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