A Randomized Controlled Feasibility Study to Evaluate the Online Delivery of Storytelling Through Music With Oncology Nurses
Bibliographic record
Abstract
ABSTRACT BACKGROUND: Oncology nurses frequently contend with intense work-related emotions stemming from their roles, which include bearing witness to suffering, managing end-of-life care, and navigating ethical dilemmas. These emotional challenges can lead to burnout, compassion fatigue, and overall psychological distress. OBJECTIVE: To determine the feasibility, acceptability, and preliminary effect of implementing Storytelling Through Music (STM) online with oncology nurses. INTERVENTION/METHODS: This study (trial registration: NCT04775524) was a 2-group, randomized wait-list controlled trial, utilizing quantitative and qualitative methods. STM is a 6-week intervention that combines storytelling, reflective writing, songwriting, and psychoeducation. Data were collected in both groups at 3 timepoints and analyzed with descriptive statistics, conventional content analysis, and nonparametric tests. RESULTS: The oncology nurses (n = 24) were primarily female (96%) and White (79%), with an average of 15.98 (range, 2-51) years of nursing experience. All STM participants completed the intervention and found it acceptable. STM participants had greater improvements in burnout, secondary traumatic stress, anxiety, depression, and posttraumatic growth. CONCLUSION: The online delivery of STM proved feasible and acceptable, demonstrating potential scalability across diverse geographic locations, and showed promise in reducing psychological distress and burnout. Future research should consider larger-scale studies with diverse participant demographics and settings to validate these findings further. IMPLICATIONS FOR PRACTICE: Results highlight the potential value of integrating expressive arts into comprehensive support programs for nurses. By implementing interventions that acknowledge and support the emotional demands of their work, healthcare organizations can better equip oncology nurses to navigate the complexities of their roles while maintaining their well-being.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".