Global Perspectives on Addressing Systemic Issues in Music Therapy Curricula and Healthcare
Bibliographic record
Abstract
The discipline of music therapy is practiced differently across the globe and over the years, in all regions of the world, the number of music therapy training programs has expanded. As such, there is a need to reflect on the differences between education and training programs, which vary according to the local culture, educational needs, theoretical background, and scope, among others. Music therapists work in a variety of environments including hospitals, community centres, regular or special education schools, speciality clinics, private practice, and long-term care homes, etc. Music therapists are tasked with navigating policies in their workplaces and balancing the needs of their clients/consumers/service users. This article written together by educators and a student aims to present brief highlights of the work of the authors whose collaboration resulted in a presentation at the European Congress of Music Therapy in June 2022. This presentation focused on the challenges and issues faced by the discipline of music therapy and health in general, based on the analysis of systemic issues in education and training, social issues, and significant issues such as multiculturalism, equity, and race around the world. These were identified as important issues for the authors who desired to raise awareness on these topics to further open ongoing dialogue. Our aim is to contribute to the establishment of dialogues between music therapy educators, allied health professionals, researchers, and students for the implementation of new courses and the updating of existing programs, expanding views on possible music therapy interventions in contemporary times.
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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 | 0.031 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".