Stories That Heal: Understanding the Effects of Creating Digital Stories With Pediatric and Adolescent/Young Adult Oncology Patients
Bibliographic record
Abstract
The purpose of this philosophical hermeneutic study was to determine if, and understand how, digital stories might be effective therapeutic tools to use with children and adolescents/young adults (AYA) with cancer, thus helping mitigate suffering. Sixteen participants made digital stories with the help of a research assistant trained in digital storytelling and were interviewed following the completion of their stories. Findings from this research revealed that digital stories were a way to have others understand their experiences of cancer, allowed for further healing from their sometimes traumatic experiences, had unexpected therapeutic effects, and were a way to reconcile past experiences with current life. Digital stories, we conclude, show great promise with the pediatric and AYA oncology community and we believe are a way in which the psychosocial effects of cancer treatment may be addressed. Recommendations for incorporating digital stories into clinical practice and follow-up programs are offered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".