Bibliographic record
Abstract
Human beings are inherently musical, and engagement with music is a natural component of individual and community life. When people face institutionalization, due to medical or mental health concerns, they may suddenly only be able to access musical involvement through a music therapy program. As music therapists build and expand clinical programming in a variety of institutional settings, justification of the role of music, and the profession’s existence, is often required based upon specific medical or psychotherapeutic outcomes. Within healthcare settings rooted in Western models of evidence-based medicine, patient’s musical engagement is often required to be justified based upon non-musical outcomes, and music therapists risk inadvertently constraining, and even pathologizing, patients’ relationships to music in order to adapt to these models. Drawing upon clinical examples from extensive music therapy experience in mental health and medical settings, this paper will explore a vision for music therapy advocacy that is grounded in music while remaining sensitive to the current demands and realities within Western models of healthcare. Just as music’s place within our education system must be grounded in more than music’s ability to further non-musical goals such as mathematical skills, so too must our vision for music’s place within healthcare become more expansive than a means through which to accomplish medical or psychotherapeutic aims. Keywords Music therapy, advocacy, institution, mental health, medicine
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".