Finding Our Voices, Singing Our Truths: Examining How Quality of Life Domains Manifested in a Singing Group for Autistic Adults
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".