Music Therapy for Pain Management for People With Advanced Cancer: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To improve mechanistic understanding, this randomized controlled trial examined anxiety, mood, emotional support, and pain-related self-efficacy as mediators of music therapy for pain management in people with advanced cancer. METHODS: People with advanced cancer who had chronic pain were randomized (1:1) to 6 weekly individual music therapy or social attention control sessions. We measured mediators and pain outcomes (pain interference and pain intensity) using self-report measures at baseline, session 4, and post-intervention. We included outcome expectancy/treatment credibility, music reward, adult playfulness, and baseline pain interference and pain intensity as moderators. RESULTS: Participants (n = 92) had a mean age of 56 years. Most were female (71.7%), white (47.8%) or Black (39.1%), and had stage IV cancer (75%). Self-efficacy was found to be a significant mediator of music therapy for pain intensity (indirect effect ab = 0.79, 95% CI 0.01-1.82) and pain interference (indirect effect ab = 1.16, 95% CI 0.02-2.51), while anxiety, mood, and emotional support were not. The mediating effect of pain-related self-efficacy was significantly moderated by baseline pain interference but not by the other moderators. CONCLUSIONS: The findings suggest that the impact of music therapy on chronic pain is mediated by self-efficacy. This knowledge can help optimize music therapy interventions for chronic pain management for people with advanced cancer by capitalizing on teaching music-based self-management strategies. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03432247.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".