Language of the Heart: Creating Digital Stories and Found Poetry to Understand Patients’ Experiences Living with Advanced Cancer
Bibliographic record
Abstract
In this article, we share our findings on patients' experiences creating digital stories about living with advanced cancer, represented through found poetry. Over a period of 12 months, patients from the program "Managing Cancer and Living Meaningfully" (CALM) completed digital stories about their experiences living with cancer. Digital stories are short, personalized videos that combine photographs, imagery, narration, and music to communicate a personal experience about a topic of inquiry. Patient interviews were conducted about the digital storytelling process. Found poetry guided the analysis technique. It is a form of arts-based research that involves using words and phrases found in interview transcripts to create poems that represent research themes. This article begins with a brief overview of the psychosocial intervention CALM, arts in healthcare, and found poetry, followed by the project background. The found poems represent themes of emotional impact, legacy making, and support and collaboration. Findings also indicate the inherently relational aspect of digital storytelling as participants emphasized the integral role of the digital storytelling facilitator. What follows is a discussion on digital storytelling, which considers the role of found poetry in representing patient voices in the research process.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".