Joint engagement and movement: Active ingredients of a music-based intervention with school-age children with autism
Bibliographic record
Abstract
BACKGROUND: The effectiveness of music-based interventions (MI) in autism has been attested for decades. Yet, there has been little empirical investigation of the active ingredients, or processes involved in music-based interventions that differentiate them from other approaches. OBJECTIVES: Here, we examined whether two processes, joint engagement and movement, which have previously been studied in isolation, contribute as important active ingredients for the efficacy of music-based interventions. METHODS: In two separate analyses, we investigated whether (1) joint engagement with the therapist, measured using a coding scheme verified for reliability, and (2) movement elicited by music-making, measured using a computer-vision technique for quantifying motion, may drive the benefits previously observed in response to MI (but not a controlled non-MI) in children with autism. RESULTS: Compared to a non-music control intervention, children and the therapist in MI spent more time in triadic engagement (between child, therapist, and activity) and produced greater movement, with amplitude of motion closely linked to the type of musical instrument. CONCLUSIONS: Taken together, these findings provide initial evidence of the active ingredients of music-based interventions in autism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".