Diversity in approaches to therapeutic voicework: Developing a model of voicework in music therapy
Bibliographic record
Abstract
The use of voicework in music therapy is emerging as an approach that requires specific attention in the literature, and the preceding chapters demonstrate its application on a global scale. Our authors are from the Australasian region (Australia, Korea, and Japan), Europe (Denmark, the Netherlands), and North America and Canada. The approaches are as diverse as the clients who are treated; they may be structured, semistructured, or unstructured and involve wordless and /or text-based voicework. The following chapter provides an integrated discussion of the work described by this new generation of researchers/clinicians who focus on the voice as a unique tool for therapeutic change. We examine the populations and the rationale for using voicework. The chapter then provides a review of the theoretical models and key concepts underpinning the work as well as an overview of how health and the therapy process are understood. The remaining part of this chapter presents the methods, including some definitions and overviews of the approaches, as well as the role of the therapist, the therapist's voice, and the client's voice. We conclude by presenting a model of voicework in music therapy that encompasses the approaches contained in this book and in the work of others. Finally, implications for future training and the areas of research needed to expand this field of music therapy are outlined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".