The Therapeutic Value of Recording in Music Therapy for Adult Clients in a Concurrent Disorders Inpatient Treatment Facility
Bibliographic record
Abstract
While recording has traditionally been viewed as a practical, adjunctive role of the music therapist, here the authors examine the skillful use of recording devices and software as fertile ground for the development of therapeutic programs with tangible benefits for adult clients in a concurrent disorders recovery setting. The integration and layering of musical composition with musical performance, digital technologies, and production, invite rich and engaging conversations about therapeutic goals, processes, and outcomes. Using methods of action research inquiry, the authors discuss how their interactions with clients through recording have yielded new insights into therapist roles and identities as well as expressions of music therapy. The case for therapy-oriented recording is outlined and a description of the authors’ research setting and data collection methods identified before a literature review on the use of recording in music therapy is provided. The authors then distinguish four types of therapeutic recording illustrated by case examples from work with clients. Their writing culminates with a discussion of challenges and benefits associated with therapeutic recording. The authors conclude that recording offers critical and rewarding yet often unrecognized opportunities for music therapists to be innovators in their field.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".