The Expanding Scope, Inclusivity, and Integration of Music in Healthcare: Recent Developments, Research Illustration, and Future Direction
Bibliographic record
Abstract
This paper is in three sections. Section One presents a historical overview of international initiatives that have expanded the role of music in healthcare, from the initial formalization of music therapy to its more research-based rehabilitation focus to recent decades that have seen an increasing role for professional and community musicians, paraprofessional music services, music-oriented service organizations, and a very large increase in medical funding for music effects. "Music Care" is a particular and comprehensive concept promoted by the Room 217 Foundation in Canada, featuring an inclusive and integrated approach to optimizing the use of music in healthcare settings. It is part of an expanding landscape of global practices and policies where music is used to address specific issues of care. Section Two is provided as an illustration of the growing scope of the concept of using music in healthcare. It reports on a multi-year project that engaged 24 long-term care homes in conducting individualized action research projects using the fundamental approach of "Music Care", empowering all caregivers, formal and informal, musicians and non-musicians, to use music to improve quality of life and care. Section Two presents only high-level results of the study focused on using music care to reduce resident isolation and loneliness. Section Three draws on the results from the study reported in Section Two to inform the potential and path to the future of music optimization in any healthcare setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| 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".