A synopsis of the MusiQual feasibility study into the effectiveness of music therapy in palliative care inpatient settings
Bibliographic record
Abstract
The research team involved in conducting the MusiQual study – carried out in Belfast, Northern Ireland by Queen’s University Belfast, Every Day Harmony Music Therapy, and Marie Curie Northern Ireland – aimed to ascertain the feasibility of carrying out a larger multicentre trial into the effectiveness of music therapy in improving the quality of life of palliative care inpatients. This synoptic paper summarises a number of publications which resulted from developing and implementing the MusiQual study. Those publications include the main findings paper (Porter et al., 2018) and a number of supplementary publications: a systematic review of the literature (McConnell et al., 2016a), a realist review of the literature (McConnell & Porter, 2016), a critical realist evaluation (Porter et al., 2017a), an outline of the theoretical model which resulted from the realist review of the literature (McConnell & Porter, 2016), and the treatment manual for music therapy in palliative care drafted for use in the potential multicentre trial and recently published (Kirkwood et al., 2019). The purpose of this synopsis is to consolidate information in one single, accessible place in order to advance knowledge in this area of work and support the evidence-informed practice of music therapists and others in this field.
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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 | 0.035 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".