Music therapy and dementia care practice in the United Kingdom: A British Association for Music Therapy membership survey
Bibliographic record
Abstract
The place of music therapy in the spectrum of musical interventions in dementia care needs to be better understood in light of the ‘supply’ and ‘demand’ of this provision. A semi-structured, online survey of British Association for Music Therapy members and affiliates was undertaken in summer 2017. It asked respondents to report on employment practice and settings, and experience in dementia-related music therapy. It asked about training received and given, and what barriers prevent wider availability of music therapy for people with dementia in the United Kingdom. Replies came from 188 people, 142 of whom were working with people with dementia. Most respondents reported working in the public or voluntary sector, but one in five was self-employed. Most (61%) were employed in residential care or hospital settings, for an average of 20 hours per week. The main factor that would increase music therapy provision in dementia care was seen as ‘greater awareness’ of music therapy amongst the general public and within the National Health Service. Nearly one-quarter (23%) thought that training and development could help increase provision. This was the largest survey undertaken to date of dementia practice by Music Therapists in the United Kingdom. It has implications for recruitment, professional development, promotion of the specialism and research.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".