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Record W4401895257 · doi:10.1093/mtp/miae015

Introduction to Guitar Techniques and Genres for the Utilization in Improvised Active Music Therapy

2024· article· en· W4401895257 on OpenAlexaff
Demian Kogutek

Bibliographic record

VenueMusic Therapy Perspectives · 2024
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsGuitarChord (peer-to-peer)Music therapyBluesPsychologyComputer sciencePsychotherapistArtAcoustics

Abstract

fetched live from OpenAlex

Abstract Guitar is a popular instrument for music therapy clinicians and students because it is portable and musically versatile. Guitar training is increasingly shared through music therapy literature, professional conferences, and workshops. However, there is still a lack of literature regarding the use of guitar improvization techniques for beginner and intermediate-level music therapists. The purpose of this manuscript is to provide the first simplified and standardized guitar methodological system for beginner and intermediate-level music therapy students and clinicians for the use in Improvised Active Music Therapy sessions in neurological rehabilitation. Improving guitar skills is important to increase confidence among students and clinicians and to enhance clinical outcomes. The manuscript is divided into two groups of exercises. The first group consists of four exercises with chord progressions. The second group consists of six strumming exercises. The strumming exercises should be combined with the chord progressions in the first group to expand upon the first position and aesthetic qualities. The exercises incorporate extended, chromatic, and suspended chords in Rock & Roll, Blues, Hip-Hop and rap, Reggae, folk, country, Latin American, and Spanish styles in key of E major. The goal is to stimulate the skill of music therapy students and clinicians, and perhaps add some extra motivation and ideas for guitar improvization skills to enhance clinical outcomes.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.086
GPT teacher head0.381
Teacher spread0.296 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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