Utilising musical microanalysis and phenomenology to enhance understanding of the impact of improvisational music psychotherapy on self-efficacy for a client with depression and anxiety
Bibliographic record
Abstract
The purpose of this mixed methods study was to understand how a client’s self- efficacy, defined as their perception of their own capability to achieve goals, is impacted by improvisational music psychotherapy conducted using MIDI instruments. Data was collected from session transcripts, several interviews with the client (Sara), and musical data. The musical microanalysis used the Music Therapy, MIDI, and MIR toolboxes within MATLAB. The collaborative data analysis incorporated the client’s perspective. Results showed that the client’s self-efficacy was influenced through multiple experiences within music psychotherapy including experiences of self-awareness and self-care; being confident and ready for change; growth and expansion outside of therapy; development and use of coping skills; and mastery and joy. By using musical microanalysis, results also indicated that certain musical features were linked to the client’s imagery, mood states, and experiences of self-efficacy. The research gives an example of how to utilise musical microanalysis to enhance the understanding of therapeutic change and processes.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".