Bibliographic record
Abstract
OBJECTIVE: Music therapy has experienced a rising demand as an adjunct therapy for symptom management among palliative care patients. We conducted a realist review of the literature to develop a greater understanding of how music therapy might benefit palliative care patients and the contextual mechanisms that promote or inhibit its successful implementation. METHOD: We searched electronic databases (CINAHL, Embase, Medline, and PsychINFO) for literature containing information on music therapy for palliative care. In keeping with the realist approach, we examined all relevant literature to develop theories that could explain how music therapy works. RESULTS: A total of 51 articles were included in the review. Music therapy was found to have a therapeutic effect on the physical, psychological, emotional, and spiritual suffering of palliative care patients. We also identified program mechanisms that help explain music therapy's therapeutic effects, along with facilitating contexts for implementation. SIGNIFICANCE OF RESULTS: Music therapy may be an effective nonpharmacological approach to managing distressing symptoms in palliative care patients. The findings also suggest that group music therapy may be a cost-efficient and effective way to support staff caring for palliative care patients. We encourage others to continue developing the evidence base in order to expand our understanding of how music therapy works, with the aim of informing and improving the provision of music therapy for palliative care patients.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".