Singing and Vocal Interventions in Palliative and Cancer Care: Music Therapists’ Perceptions of Usage
Bibliographic record
Abstract
BACKGROUND: Music therapists in palliative and cancer care settings often use singing and vocal interventions. Although benefits for these interventions are emerging, more information is needed on what type of singing interventions are being used by credentialed music therapists, and what goal areas are being addressed. OBJECTIVE: To assess music therapists' perceptions on how they use singing and vocal interventions in palliative and cancer care environments. METHOD: Eighty credentialed music therapists from Canada and the United States participated in this two-part convergent mixed-methods study that began with an online survey, followed by individual interviews with 50% (n = 40) of the survey participants. RESULTS: In both palliative and cancer care, singing client-preferred music and singing for relaxation were the most frequently used interventions. In palliative care, the most commonly addressed goals were to increase self-expression, improve mood, and create a feeling of togetherness between individuals receiving palliative care and their family. In cancer care, the most commonly addressed goals were to support breathing, improve mood, and support reminiscence. Seven themes emerged from therapist interviews: containing the space, connection, soothing, identity, freeing the voice within, letting go, and honoring. CONCLUSIONS: Music therapists use singing to address the physical, emotional, social, and spiritual goals of patients, and described singing interventions as accessible and effective. Further research is recommended to examine intervention efficacy and identify factors responsible that contribute to clinical benefit.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".