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Record W2923422338 · doi:10.1177/1359457519834533

The MusiQual treatment manual for music therapy in a palliative care inpatient setting

2019· article· en· W2923422338 on OpenAlexfundno aff
Jenny Kirkwood, Lisa Graham‐Wisener, Tracey McConnell, Sam Porter, Joanne Reid, Naomi Craig, Conall Dunlop, Catherine Gordon, Daniel Thomas, Jo Godsal, Aisling Vorster

Bibliographic record

VenueBritish Journal of Music Therapy · 2019
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
FundersQueen's UniversityPublic Health AgencyQueen's University Belfast
KeywordsMusic therapyMedicinePalliative carePsychological interventionFidelityScope (computer science)Quality of life (healthcare)PsychotherapistPhysical therapyNursingPsychology

Abstract

fetched live from OpenAlex

This article presents the treatment manual developed during the MusiQual feasibility study carried out in Belfast by Queen’s University Belfast, Every Day Harmony Music Therapy, and Marie Curie Northern Ireland. The MusiQual study considered the feasibility of a multicentre randomised trial to evaluate the effectiveness of music therapy in improving the quality of life of hospice inpatients (protocol: McConnell et al. results: Porter et al.). The procedures in the manual are based fully on those implemented by the Music Therapists during the feasibility study, and it also incorporates the theoretical model defined and published following the realist review of the literature (McConnell and Porter). The manual is presented in the format in which it would be used in the potential future phase III multicentre randomised control trial. It represents a flexible approach to provide enough scope for practicing therapists to adapt their interventions to individual clients as is best practice in music therapy. It aims to provide stable guidelines both to ensure treatment fidelity in a future trial of music therapy for palliative care inpatients and to act as a relevant guide for Music Therapists practicing in this field.

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 categoriesInsufficient 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 score0.917
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.356
Teacher spread0.299 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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