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Record W2897266420 · doi:10.1177/1359457518799854

Music therapy and dementia care practice in the United Kingdom: A British Association for Music Therapy membership survey

2018· article· en· W2897266420 on OpenAlexaboutno aff
Justine Schneider

Bibliographic record

VenueBritish Journal of Music Therapy · 2018
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordsMusic therapyDementiaPromotion (chess)Psychological interventionMedicineQuarter (Canadian coin)Occupational therapyNursingPsychologyPsychiatryPolitical scienceHistory

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.158
GPT teacher head0.374
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2018
Admission routes1
Has abstractyes

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