Understanding Music Care and Music Care Delivery in Canadian Facility-based Long Term Care
Bibliographic record
Abstract
In light of current confluences in long term care (LTC), a renewed understanding of music care and music care delivery is needed in Canadian LTC facilities. Ten domains of music care are presented as a conceptual framework in which to clarify these new realities as well as form a basis for music optimization in LTC. In this mixed method study, seven emergent factors which influence music care delivery from a phase one qualitative study in five Ontario LTC homes form the basis of a phase two, pan-Canadian survey in 50 LTC homes. Factors for music care delivery include attitudes towards music care, the nature of music, facility location and design, planning and sustainability, education and awareness, and gaps between theory and practice. Research questions in this exploratory sequential design explore how music care is understood and delivered. Results show several key aspects of understanding music care in Canadian LTC facilities: music is essential, music impacts quality of life and quality of care, music strengthens social agency, staff values music less than residents, music care needs to be understood to be optimized, music care and music therapy are distinct, and music enhances culture change. Phase two findings enriched phase one findings with both congruencies and incongruencies of music care delivery. Six recommendations for LTC leadership are posited. Music care education is proposed as a significant means for music care understanding, optimization and delivery that enhances the resident experience through improved quality of life and quality of care.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".