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Record W2982440146 · doi:10.47513/mmd.v11i4.707

Uncommon music making: The functional roles of music in design for healthcare

2019· article· en· W2982440146 on OpenAlexaff
Elif Özcan, Lois Frankel, Jesse Stewart

Bibliographic record

VenueMusic and Medicine · 2019
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsMusicalHealth carePsychologyCohesion (chemistry)Health professionalsMusic therapyProcess (computing)Group cohesivenessApplied psychologyPublic relationsComputer scienceVisual artsSocial psychologyPolitical scienceArtPsychotherapist

Abstract

fetched live from OpenAlex

In this paper, we discuss some uncommon settings and roles for music, demonstrating how music can aid in the design and implementation of socially responsible healthcare products that are encouraging, inclusive, and sensitive to critical contexts. We review three music-inspired design cases (CareTunes: Musical Alarms for Critical Care, Music and Senior Exercise, and We Are All Musicians and the Adaptive Use Musical Instrument) in which the authors took part. The literature review and the analysis of the case studies provide us with the following insights: music enhances sensory experiences, facilitates physical engagement with the world, music can guide medical professionals in critical contexts, and music creates social cohesion. All of these projects demonstrate the importance of involving participants (users or performers) in the process to address their life experiences. Thus, the use of music in design applications is experienced as a positive influence that can facilitate wellbeing for community members, persons with disabilities, medical patients, and healthcare professionals in the workplace.

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 score0.545
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.167
GPT teacher head0.373
Teacher spread0.205 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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