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Record W3038580246 · doi:10.15845/voices.v20i2.2554

Finding Our Voices, Singing Our Truths: Examining How Quality of Life Domains Manifested in a Singing Group for Autistic Adults

2020· article· en· W3038580246 on OpenAlexaff
Laurel Young

Bibliographic record

VenueVoices A World Forum for Music Therapy · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsSingingPsychologyQuality of life (healthcare)AutismTransformative learningMusic therapyDevelopmental psychologyQualitative researchWell-beingPopulationMedicinePsychotherapistSociologySocial science

Abstract

fetched live from OpenAlex

A growing body of literature indicates that participation in singing groups has a range of health and wellbeing benefits for the general adult population and for various adult cohorts with specific challenges/needs. However, no research had been conducted on potential benefits of group singing for Autistic adults. Furthermore, the neurodiversity movement rejects a biomedical approach to autism and champions the need for supports that will empower individuals on the autism spectrum to participate in society on their own terms. This aligns well with community music therapy (CoMT) philosophy which maintains that all persons have a right to access and participate in music experiences that promote personal health and wellbeing as well as serve as an expression of individuality, culture, and community. Therefore, the present research investigated how quality of life (QoL) variables (considered as components/determinants of health and wellbeing) manifested for eight Autistic adults who participated in 12 group singing sessions. A mixed methods concurrent transformative design was used with priority given to qualitative data. Results illustrate how subdomains contained within overarching QoL domains of Being, Belonging, and Becoming were realized by the group participants. Limitations of the study as well as implications for practice and research are presented.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.119
GPT teacher head0.345
Teacher spread0.226 · 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 designBench or experimental
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

Citations12
Published2020
Admission routes1
Has abstractyes

Explore more

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