The use of music in the treatment of chronic pain: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Music is a promising strategy to address the physical, psychological, and social needs of people with chronic pain. To better understand its potential in the treatment of chronic pain, this study aims to assess the state of knowledge regarding the effects of music in the context of chronic pain treatment. METHODS: A scoping review was conducted in eight databases using music, chronic pain, and treatment concepts and associated keywords. Studies were included in the review if they reported some effects of any form of music on chronic pain or concomitant conditions. RESULTS: Sixty-three studies were identified. Results showed numerous benefits of music-based interventions on chronic pain and common concomitant difficulties including emotional regulation, anxiety and depression symptoms, and social issues. Though literature supports varied forms of music-based interventions, those that account for participants' preferences and that encourage self-management and autonomy appeared to be the most effective. CONCLUSIONS: Despite the benefits of music in the management of pain and concomitant difficulties, there remain few examples of applied music interventions in services designed for people who experience chronic pain. More research is needed to identify the musical modalities that would be the most adapted and effective to complement chronic pain services.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".