Developing a best-practice agenda for music therapy research to support informal carers of terminally ill patients pre- and post-death bereavement: a world café approach
Bibliographic record
Abstract
Abstract Background Informal carers of terminally ill patients play a vital role in providing palliative care at home, which impacts on their pre- and post-death bereavement experience and presents an up to 50% greater risk for mental-health problems. However, developing and implementing effective bereavement support remains challenging. There is a need to build the evidence base for music therapy as a potentially promising bereavement support for this vulnerable population. This study aimed to co-design an international best practice agenda for research into music therapy for informal carers of patients pre- and post-death bereavement. Methods Online half day workshop using a World Café approach; an innovative method for harnessing group intelligence within a group of international expert stakeholders (music therapy clinicians and academics with experience of music therapy with informal carers at end-of-life). Demographics, experience, key priorities and methodological challenges were gathered during a pre-workshop survey to inform workshop discussions. The online workshop involved four rounds of rotating, 25-minute, small group parallel discussions using Padlet. One final large group discussion involved a consensus building activity. All data were analysed thematically to identify patterns to inform priorities and recommendations. Results Twenty-two consented and completed the pre-event survey (response rate 44%), from countries representing 10 different time zones. Sixteen participated in the workshop and developed the following best practice agenda. The effectiveness of music therapy in supporting informal carers across the bereavement continuum should be prioritised. This should be done using a mixed methods design to draw on the strengths of different methodological approaches to building the evidence base. It should involve service users throughout and should use a core outcome set to guide the choice of clinically important bereavement outcome measures in efficacy/effectiveness research. Conclusions Findings should inform future pre- and post-death bereavement support research for informal caregivers of terminally ill patients. This is an important step in building the evidence base for commissioners and service providers on how to incorporate more innovative approaches in palliative care bereavement services.
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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 | 0.233 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.031 | 0.019 |
| Open science | 0.009 | 0.035 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".