An agenda for excellence: the role of music therapy for people living with chronic pain
Bibliographic record
Abstract
Research on the role of music and music therapy conducted for several decades reflect a dearth of literature on the health and well-being benefits of music-based and music therapy interventions for people living with chronic pain. To support advances of research on music therapy for chronic pain, the authors met regularly as members of a Special Interest Group on music, music therapy and chronic pain. The authors, from five different countries, representing the perspectives of music therapy, community music, pain medicine, and service user, discussed theoretical and methodological issues to be addressed in future studies. This article summarizes our collective thoughts in relation to priority questions for future research in music therapy and chronic pain, ethical challenges, research methods, practice techniques and future priority areas. The article sets an agenda for high quality research and practice for music therapy in the treatment and care of people living with chronic pain.
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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 | 0.097 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.026 | 0.032 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".