Novel Screening Tool and Considerations for Music Therapists Serving Autistic Individuals via Telehealth: Qualitative Results from a Survey of Clinicians’ Experiences
Bibliographic record
Abstract
During the COVID-19 pandemic, music therapists transitioned services from in-person to telehealth due to health and safety concerns. Though online delivery of music therapy services for autistic individuals occurred prior to 2020, the number of North American music therapists using telehealth with autistic clients rose substantially during the pandemic. The current paper's objective was to delineate music therapists' perceptions regarding factors that helped or hindered autistic persons' engagement in online music therapy sessions. In total, 192 participants completed the survey. Qualitative content analysis of an open-ended question identified seven overarching themes regarding the benefits and challenges of telehealth music therapy for autistic clients. Findings were used to create a screening tool to help music therapists evaluate autistic persons' suitability for telehealth and meet the needs of those who can benefit from telehealth music therapy.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".