Storytelling Through Music With Parents Whose Children Have Died From Cancer: A Randomized Controlled Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: Bereaved parents have significantly higher morbidity and mortality than non-bereaved parents. Despite national guidelines recommending bereavement care, resources for bereaved parents are scarce. Most intervention studies lack empirical evidence of effectiveness or alignment with key theoretical concepts. AIMS: To evaluate the feasibility of a 6-week intervention with parents of children who have died from cancer. Storytelling Through Music (STM) combines multiple modalities of expression (storytelling, reflective writing, songwriting) and psychoeducation to facilitate loss- and restoration-oriented coping by creating a legacy piece (self-written story paired with a song) to help bereaved parents adapt to a life-long process of finding meaning after loss. METHODS: Two-group, randomized controlled trial, utilizing multiple methods. Participants were randomized to STM or waitlist control. The intervention is delivered online and in a group setting. Descriptive statistics were used for feasibility data, content analysis to evaluate open-ended acceptability questions, and RM ANOVA to evaluate the differences between psychosocial, coping, and grief outcomes. RESULTS: Twenty-three parents were enrolled. Average age was (range: 32-68) and the child's average age was 18.9 (range: 1.5-35). This study indicates that the online delivery of STM is feasible and acceptable and provides preliminary evidence of reducing prolonged grief and loneliness. CONCLUSIONS: STM is a theoretically driven, innovative approach to addressing grief in a high-risk, underserved population. Findings suggest STM can be delivered online and is acceptable to participants. Adding music to storytelling and reflective writing provides a unique expression and preliminary data suggests improvements in psychosocial well-being, coping, and grief intensity.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".