Use of the Harp by North American Music Therapists in Oncology/Palliative Care: A Survey Study
Bibliographic record
Abstract
The continuum of oncology/palliative care presents complex bio-psychosocial and spiritual needs of patients that music therapy seeks to address. While the most commonly-reported music therapy interventions for cancer patients use receptive methods, in particular live music presented by the music therapist, the literature is sparse regarding the use of musical instruments in these contexts. Evidence from both music therapy and adjunct music in healing practices indicates that live harp music can be of benefit for specific goals such as pain management, comfort and relaxation, reduction of anxiety, and improvement in quality of life. The purpose of this study was to explore the use of the harp by music therapists in Canada and the United States of America in oncology/palliative care. There were 23 respondents fitting the criteria of using the harp in oncology/palliative care, from a total of 201 credentialed music therapists who answered an English-language online survey consisting of open-ended and close-ended questions. Results showed that the therapists surveyed perceived it to be a useful music therapy instrument in cancer care, particularly using receptive methods to create a healing environment and increase comfort. Acoustic, aesthetic, and archetypal qualities emerged as bearing potential therapeutic impact. Risks and contraindications highlighted the archetypal connection with angels, heaven, and death. Training, study limitations, potential implications for the profession, and future research are discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".