The Effect of Music Therapy on Anxiety in Patients who are Terminally Ill
Bibliographic record
Abstract
BACKGROUND: The literature supporting the use of music therapy in palliative care is growing. However, the number of quantitative research studies investigating the use of music therapy in palliative care, and specifically anxiety, is limited. OBJECTIVE: The aim of this research project was to examine the effectiveness of a single music therapy session in reducing anxiety for terminally ill patients. DESIGN: A randomized-controlled design was implemented and the following hypotheses tested. There will be a significant difference between the experimental and control groups on anxiety levels as demonstrated by the anxiety measurement of the Edmonton Symptom Assessment System (ESAS), and heart rate. The experimental group received a single music therapy intervention and the control group received a volunteer visit. SETTING/SUBJECTS: Twenty-five participants with end-stage terminal disease receiving inpatient hospice services were recruited. RESULTS: The first hypothesis was supported. Results demonstrated a significant reduction in anxiety for the experimental group on the anxiety measurement of the ESAS (p = 0.005). A post hoc analysis found significant reductions in other measurements on the ESAS in the experimental group, specifically pain (p = 0.019), tiredness (p = 0.024) and drowsiness (p = 0.018). The second hypothesis was not supported. CONCLUSIONS: The study supports the use of music therapy to manage anxiety in terminally ill patients. Further studies are required to examine the effect of music therapy over a longer time period, as well as addressing other symptom issues.
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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 | 0.003 |
| 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".