Music Therapy Intervention to Reduce Symptom Burden in Hospice Patients: A Descriptive Study
Bibliographic record
Abstract
Background: Music therapy (MT) offers benefits of improved symptom relief and quality of life at the end of life, but its impact on hospice patients and caregivers needs more research. Objective: To assess the impact of MT intervention on symptom burden and well-being of hospice patients and caregivers. Methods: A total of 18 hospice patients, selected based on scores ≥4 on the revised Edmonton Symptom Assessment System (ESAS-r) items on pain, depression, anxiety, or well-being, participated in MT sessions provided by a board-certified music therapist. Over a period of 2-3 weeks, 3-4 MT sessions were conducted for each. Patient Quality of life (QOL) was assessed using the Linear Analogue Self-Assessment (LASA). Depression and anxiety were measured with the Patient Health Questionnaire-4 (PHQ-4). For the 7 caregivers enrolled, stress levels were measured using the Pearlin role overload measure and LASA. Results: Patients reported a reduction in symptom severity and emotional distress and an increase in QOL. All patients endorsed satisfaction with music therapy, describing it as particularly beneficial for stress relief, relaxation, spiritual support, emotional support, and well-being. Scores on overall QOL and stress were worse for caregivers. Conclusion: This study provides evidence that MT reduces symptom burden and enhances the quality of life for hospice patients. Hospice patients and their caregivers endorsed satisfaction with MT. Given the benefits observed, integrating MT into hospice care regimens could potentially improve patient and caregiver outcomes. Larger studies should be conducted to better assess the impact of MT in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".