Music Intervention as a Tool in Improving Patient Experience in Palliative Care
Bibliographic record
Abstract
BACKGROUND:: The pain, anxiety, and stress associated with end-of-life care are paramount issues to address for both patients and their families. Reduction in these factors could translate to improved quality of life. OBJECTIVE:: We studied the effect of adding music to standard care for patients receiving a hospice or palliative care consult at 2 hospitals in the Care New England health-care system. In this mixed quantitative and qualitative study, we implemented live music intervention sessions. DESIGN/MEASUREMENTS:: Outcomes include symptom burden pre- and post-intervention using the Edmonton Symptom Assessment Scale, opioid use in equivalent time periods before and after the music intervention, and qualitative personal narratives of patients' and families' experiences with the music. RESULTS:: There were significant decreases in pain, anxiety, nausea, shortness of breath, and feelings of depression along with significant increase in feelings of well-being. Opioid use in time periods after the music intervention trended toward decreased usage when compared to the equivalent time period before. Finally, compiled personal narratives of patients' and families' experiences of the music intervention demonstrated common themes of spirituality, comfort, relaxation, escape, and reflection. CONCLUSIONS:: This project demonstrated the beneficial effects of music in a patient population that struggles with symptom management when only pharmacologic management is used. These data elucidate biological and psychosocial factors that are positively impacted by the intervention. With additional evidence in music as well as other artistic modalities, it is promising that arts-based programs in inpatient hospice and palliative care settings will continue to expand and flourish.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".