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Record W2184693415

Tango Improvisation in Music Therapy/L'improvisation De Style Tango En Musicothérapie

2014· article· fr· W2184693415 on OpenAlexaboutno aff
Demian Kogutek

Bibliographic record

VenueCanadian journal of music therapy · 2014
Typearticle
Languagefr
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsImprovisationMusic therapyPsychologyMusicalStyle (visual arts)Context (archaeology)SingingDanceVisual artsArtHumanitiesHistoryPsychotherapist
DOInot available

Abstract

fetched live from OpenAlex

The use of clinical improvisation has been extensively researched and written about (Aigen, 2005; Ansdell, 1995; Bruscia, 1987; Lee, 2003; Lee & Houde, 2010; Nordoff & Robbins, 1977/2007; Pavlicevic, 1991; Ruud, 1998; Wigram, 2004). However, a less explored research area of clinical improvisation has been the use of different styles of music. Lee (2003) and Lee and Houde (2010) posited that music therapists should be knowledgeable about multicultural styles and the general theoretical makeup of different music from around the world. According to Aigen (2005), Paul Nordoff reported a remarkable experience with the first client he worked with in a music therapy context. He observed that while the boy seemed content and serene when a Chinese pentatonic scale was played, the boy reportedly cried when Nordoff altered the tones to a Japanese pentatonic. Nordoff went back and forth between the two scales, and each time, he observed the same reaction. If two different musical styles can generate two completely different emotional responses, what are the implications of using different styles of music in clinical improvisation? How then, can these implications be utilized in a therapeutic setting?Purpose of the StudyWhile I was growing up and studying music in Buenos Aires, Argentina, tango music was an integral part of my life. After immigrating to Canada at the age of 22, my connection to tango diminished somewhat, maybe because the environmental incentive was not present any more. Many years later, I began incorporating tango music in clinical improvisation sessions as a master's degree student at Wilfrid Laurier University in Waterloo, Ontario, Canada. During these individual sessions, I noticed clients who had a tendency to play similar rhythmic and melodic patterns throughout improvisations. My aim as a student was on incorporating not only tango, but also different styles of music, instrumental arrangements, and improvisational techniques in order to expand clients' musical vocabulary and communication while assessing the clients' acquirement of musical patterns and ideas over time.It was because of my clinical work that I decided to conduct this research. The purpose of this research study was to incorporate tango music in improvisational music therapy. The objective was to expand each client's level of musical communication by analyzing the qualities of improvised tango, specifically dynamics, rhythm, melodic patterns, and tempo, ultimately linking the participant's musical expansion to the development of the therapeutic relationship.In order to understand the implications of tango style during clinical improvisation sessions, I formulated the following two questions:* How do the musical components of tango expand the client's level of musical communication?* How do the musical components of tango affect the therapeutic process?MethodologyResearch DesignThis qualitative research used a grounded theory approach. The goal of this research method is to develop interrelated concepts that can describe reality and at the same time generate new ideas (Amir, 2005; Glaser & Strauss, 1967). Semeijsters (1997) stated that reality is not described by means of an already existing theory and hypotheses; instead, these can be generated from and become grounded in the reality of a research study. This process requires the researcher's total immersion in the data in order to become intimately acquainted with the data and develop a detailed knowledge of it.The research began with the collection of data, and through this process I was able to identify patterns, relationships, concepts, and categories. This phase is called open coding. The second step, axial coding, was then done. This involved procedures for connecting and relating categories and subcategories found in the open coding (Wheeler, 2005). Most grounded theory research, including the one described in this article, also incorporates data related to self-reports, audio recording, and observations (Smeijsters, 1997). …

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.000

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 head0.037
GPT teacher head0.222
Teacher spread0.185 · 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 designNot applicable
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

Citations0
Published2014
Admission routes1
Has abstractyes

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