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Record W2900942819 · doi:10.1080/08098131.2018.1542615

The experiences of professional artists in clinical improvisation: a pilot study

2018· article· en· W2900942819 on OpenAlexafffund
Deborah Seabrook

Bibliographic record

VenueNordic Journal of Music Therapy · 2018
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsConcordia University
FundersFonds de Recherche du Québec-Société et Culture
KeywordsImprovisationMusic therapyVisual artsArtPsychologyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

Clinical improvisation is a method used in music therapy to address the health and well-being needs of individuals within a variety of client populations. While professional artists can experience personal challenges as part of their vocation, the applications of clinical improvisation for professional artists as a client group had not previously been investigated. The aim of this pilot study was thus to explore the experiences of professional artists in clinical improvisation. Eighteen professional artists, of whom 13 were classical musicians, participated in clinical improvisation with a music therapist. Qualitative data were collected through participant interviews with 16 participants. Thematic analysis highlighted the artists’ experiences in terms of: (1) requirements of engaging in clinical improvisation; (2) experiences of self; (3) relationship with the music therapist; and (4) a unique experience for classical musicians. A discussion elucidates how these experiences of professional artists are congruent with those of other client populations in clinical improvisation and further research is suggested.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0020.004
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 head0.150
GPT teacher head0.461
Teacher spread0.311 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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