The use of music to feel happy and safe exemplified by the case of Debbie, a First Nations teenager diagnosed with ASD
Bibliographic record
Abstract
Abstract This presentation introduces a case study that aims to show how music can be used to improve the quality of life for individuals diagnosed with Autism Spectrum Disorder (ASD). Background There is extensive research on music interventions for individuals with ASD but there is a lack on research within Indigenous context. This presentation focuses on one of five cases, a teen called Debbie, from a research project on the meaning of music for First Nations children in British Columbia, Canada, diagnosed with ASD. Methodology Ethnographic field studies strongly influenced by Indigenous research methodologies were conducted in 2013 and 2014. The material consists of transcribed interviews, observations, and videotaped observations and music interventions. Ethical aspects This research project was approved by the ethical committee at the University of Eastern Finland. Informed consent was given by all who participated in the study and consent was negotiated throughout the research process. All names were changed and tribal affiliation omitted. Results Debbie uses music in all aspects of her life to feel happy and safe. She listens to it, watches videos, sings and dances whenever possible. At home, in school and at the after school club, music is a big part of her structure. Contemporary pop and dance music has been her preference until she recently made and played an Aboriginal drum. Closing remarks There is a scarcity of research on music and autism within Indigenous context. Hopefully, this case can inspire to future research and influence support systems and interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".