P-69 Music care training program: integrating music into regular care practice
Bibliographic record
Abstract
Background In Canada, music is identified as a non-pharmacological, cost-effective and person-centred approach across the care spectrum, including palliative care. Caregiver confidence and music medicine knowledge translation are barriers to its use. Music Care Training (MCT) is an education program assisting care partners in the effective and responsible use of music in care. Aim Provide musical tools and theoretical context to enrich care practice; Develop understanding of music care approach, musical elements and evidence base for using music in care; creating music care initiative for care context. Methods Hold music care trainings in identified Canadian city-centres; pre-test questionnaire, training in on-site classes of maximum 30, post-test questionnaire, three month online follow up, measure social return on investment (SROI). Results • 800 care partners trained in 36 months, 19% hospice palliative care context, 56% long term care context (advanced dementia care). • Level 1 – caregiver confidence increased in ability to use music care kit, design music care plan and explain music as an approach to care. • Level 2 – caregiver confidence increased to describe music’s effects on whole person care, including neuroplasticity, pain management, mood, memory and meaning-making. • Level 3 – demonstrated music care initiative implemented in 86% of participants. • 95.51% student satisfaction level. • Key performance indicators developed to measure SROI. Caregivers Stories ‘I have used the training several times. One resident came out of a showering experience sobbing, shivering, rocking, crying and moaning shock. I was able to use the call and response, the humming, the singing and the breathing to calm her and lull her to sleep. Without your training I may have been at a loss as I have never seen this resident so upset.’ Conclusions Music Care Training is giving care partners in Canada the knowledge and confidence needed to use music care. It is a replicable model for the UK.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".