Association Between Music Therapy Techniques and Patient-Reported Moderate to Severe Fatigue in Hospitalized Adults With Cancer
Bibliographic record
Abstract
PURPOSE: Cancer-related fatigue is a prevalent, debilitating symptom that contributes to increased health care utilization among hospitalized patients. Music therapy is a nonpharmacological intervention that uses active (eg, singing, selecting songs) and passive (eg, listening) techniques. Preliminary evidence from small trials suggests a potential benefit for cancer-related fatigue in the inpatient setting; however, it remains unclear which techniques are most effective. METHODS: A cross-sectional mixed-methods study was performed to compare cancer-related fatigue before and after active or passive music therapy. Cancer-related fatigue was captured via the Edmonton Symptom Assessment Scale fatigue item. Patients were asked to provide postsession free-text comments. RESULTS: A total of 436 patients (mean [standard deviation] age, 62.2 [13.4] years; n = 284 [65.1%] women; n = 294 [67.4%] white; active music therapy n = 360 [82.6%]; passive music therapy n = 76 [17.4%]) with a range of primary malignancies participated. Active music therapy was associated with a 0.88-point greater reduction in cancer-related fatigue (95% CI, 0.26 to 1.51; P = .006; Cohen’s D, 0.52) at postsession as compared with passive music therapy when restricting the analysis to patients who rated their baseline cancer-related fatigue as moderate to severe (ie, ≥ 4; n = 236 [54.1%]). Free-text responses confirmed higher frequencies of words describing positive affect/emotion among active music therapy participants. CONCLUSIONS: In a large sample of inpatient adults with diverse cancer disease types, active music therapy was associated with greater reduction in cancer-related fatigue and increased reporting of positive affect/emotions compared with passive music therapy. Additional research is warranted to determine the specific efficacy and underlying mechanisms of music therapy on cancer-related fatigue.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".