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Record W1491474725

Clinical Improvisation Techniques in Music Therapy - A Guide for Students, Clinicians and Educators

2013· article· en· W1491474725 on OpenAlexaboutno aff
Heather Fletcher

Bibliographic record

Venue˜The œAustralian journal of music therapy · 2013
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordsImprovisationMusic therapyMusicalPsychologyPublishingVisual artsArtPsychotherapistLiterature
DOInot available

Abstract

fetched live from OpenAlex

Carroll, D. & Lefebvre, C. (2013). Clinical improvisation techniques in music therapy - A guide for students, clinicians and educators. Springfield: Charles C. Thomas Publisher Ltd.Both Debbie Carroll and Claire Lefebvre are music therapy professors at the Universite du Quebec a Montreal (UQAM), where they have been educating and supervising since 1985 and 1 986 respectively. This book was borne out of their work with at UQAM and an identified need to provide a clear and systematic approach to teaching improvisational techniques to undergraduate music therapy students (p.4). The authors acknowledge the book is based on the taxonomy of clinical improvisation techniques as described by Bruscia (1987), and go on to describe the approach they have developed for clinically applying these techniques.There are just over 100 pages to this book and, with the appendices taking up the last quarter of the book, it is a relatively quick read. The book is divided into two parts. The layout Is logical and easy to follow and the spiral binding makes it very user friendly.Part One describes the taxonomy of clinical improvisation techniques, dividing these into two interconnected categories: musical techniques and verbal techniques. Each category starts with a prelude and summary list of the musical techniques, followed by a detailed description of each technique. The musical techniques have been grouped into five sub-categories and include general therapeutic goals and clinical contexts. The verbal techniques have been organised into two sub-categories, based on the music therapist taking on a more or less directive approach. From a clinician's point of view, there was very little in Part One which was new to me, and I therefore found it quite affirming to read. Further, reading this section was also a good exercise in reflective practice, as it drew out the 'why, when, what and how' music therapists do what they do. This is helpful not only for individual practitioner's practice, but also in helping to articulate these concepts to others.Part Two focuses on applying the techniques from Part One. It is divided into four sections: expanding clinical and musical resources; role-play exercises; guidelines for working with the clients playing; and six role-play exercises with predetermined musical and clinical parameters. Whereas Part One breaks clinical improvisation down into its component parts, Part Two starts to put them back together and demonstrates how the whole is greater than the sum of its parts. In doing so, the reader begins to see how clinical musicianship can be developed. To make the best use of this section, it would be helpful to work through it with a tutor, colleague or willing volunteer, trying out and practising the various exercises and then of course reflecting on your experiences. Thinking back to when I trained as a music therapist, I believe I would have found this a very useful resource.The book contains five Appendices, which outline the clinical techniques used in Improvisational Music Therapy (Bruscia, 1987) and the differences between Bruscia's groupings of those clinical techniques and the present authors; the connections among certain techniques; students' exploration of the potential therapeutic effects of intervals; scales and other useful musical structures; and three improvisational models of music therapy; Juliette Alvin's Free Improvisation; Paul Nordoff & Clive Robbins' Creative Music Therapy; and Mary Priestley's Analytical Music Therapy. …

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)0.005
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.124
GPT teacher head0.462
Teacher spread0.338 · 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 teacher head, not a consensus.

Study designOther design
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

Citations16
Published2013
Admission routes1
Has abstractyes

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