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23 Findings from a pilot and feasibility trial of a music therapy intervention in specialist palliative care

2018· article· en· W2885195445 on OpenAlexaboutno aff
Lisa Graham‐Wisener, Joanne Reid, Tracy McConnell, Kerry McGrillen, Jenny Kirkwood, Miriam McKeown, Mike Clarke, Joan Regan, Naomi Hughes, Sam Porter

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2018
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordsAttritionMusic therapyMedicineQuality of life (healthcare)Palliative careIntervention (counseling)Randomized controlled trialPhysical therapyNursingInternal medicine

Abstract

fetched live from OpenAlex

Introduction Music therapy aligns to the holistic approach to palliative and end-of-life care (PEOLC) with increased prevalence in PEOLC settings (Graham-Wisener et al. 2018). Despite this there is a dearth of high-quality evidence examining the impact of music therapy towards end of life on quality of life (McConnell et al. 2016). Aims The aim of this pilot and feasibility study was to: test procedures; outcomes and validated tools; estimate recruitment and attrition rates; and calculate the sample size required for a phase III randomised controlled trial. Methods A single-centre pilot and feasibility trial with patients admitted to a specialist palliative care inpatient unit within the United Kingdom. Participants were randomised (1:1) to either a music therapy intervention of two 30–45 min sessions of music therapy per week for three consecutive weeks or usual care. The primary outcome measure was to evaualte the feasibility of administering the McGill Quality of Life Questionnaire (MQoL) baseline with follow-up measures at one, three and five weeks. Results 51 participants were recruited to the trial over a 12 month period. Feasibility of administering music therapy intervention and attrition rates identified one-week follow-up as an appropriate primary outcome. Results suggest a likely effect on MQoL total score between intervention and control arms in particular the existential subscale. Conclusion The current study resolved a number of issues towards informing robust procedures for a phase III RCT. This data is urgently needed to ensure an evidence-based decision on inclusion of music therapy in palliative care services. References . Graham-Wisener L, Watts G, Kirkwood J, Harrison C, McEwan J, Porter S, Reid J, McConnell TH. Music therapy in UK palliative and end-of-life care: A service evaluation. BMJ supportive & palliative care2018. . McConnell T, Scott D, Porter S. Music therapy for end-of-life care: An updated systematic review. Palliative Medicine2016;30(9):877–883.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.214
GPT teacher head0.465
Teacher spread0.251 · 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 designObservational
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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