Using music-adapted technology to explore Bruscia’s clinical techniques introduced in autism research: Pilot study
Bibliographic record
Abstract
This pilot research investigated eight most commonly used Bruscia’s (1987) clinical improvisation techniques utilised in music therapy with autistic clients: imitating, reflecting, synchronising, extending, symbolising, holding, incorporating, and rhythmic grounding (Skinner, Kurkjian & Ahonen, 2020). The techniques were explored with research participants (music students), by isolating and implementing each technique in eight short improvisations. Improvisations were recorded using LogicPro connected to MalletKAT instruments. Improvisations were analysed using music-adapted technology, the MIDI Toolbox designed for MATLAB, a multi-paradigm numerical computing environment and proprietary programming language developed by MathWorks, and the Music Therapy Toolbox (MTTB) (Erkkilä, Lartillot, Luck, Riikkila & Toiviainen, 2004). In addition, participants provided their subjective experience of each improvisation in a questionnaire format. The research questions included: 1) How will Bruscia’s eight fundamental clinical improvisation techniques be represented in MATLAB/MTTB in terms of both individual ways of playing and musical relationships? 2) How will the use of each isolated improvisation technique impact the participant’s experience of musical connection, influence, and expression? Through the combination of musical analysis and qualitative thematic analysis, insights relating to the effective implementation and purposeful use of imitation, synchronisation, holding, and rhythmic grounding were realised. The musical data generated from MATLAB/MTTB demonstrated how researchers implemented the techniques and trends in the participant’s playing. In addition, the questionnaires provided insights into how each technique influenced the participant’s ability to express and connect, as well as their perception of the researchers’ musical influence. These results may be used to inform both music therapists and future related research.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".