Understanding How Western-Trained Music Therapists Incorporate Chinese Culture In Their Practice In China: An Ethnographic Study
Bibliographic record
Abstract
Although music therapists work with Chinese people in the United States, Canada, Australia, and China, little is known about how these music therapists incorporate Chinese culture in their practice in order to provide culturally-responsive music therapy. This ethnographic research study aimed to understand how Western-trained music therapists incorporated Chinese culture in their practice in China. The researcher observed music therapy sessions of two Western-trained music therapists in a neurologic rehabilitation and mental rehabilitation department in a hospital in China. The two music therapists, two of their clients, and two facility professionals were interviewed. Six categories were identified in the analysis. The findings indicated that Western-trained music therapists incorporated Chinese culture in their session by: using “mixture” music, developing and maintaining guanxi with various individuals, using multiple models of music therapy, using a variety of active and receptive experiences, using the Chinese Mandarin language, and acknowledging that music therapy is a natural therapy. Overall, themes of mixture were in several of the categories suggesting that music therapy with Chinese people is a “mixture” music therapy. Future research by Chinese music therapists is needed to understand varying perspectives of culturally-responsive music therapy for Chinese people.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".