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Record W2589066033 · doi:10.1177/1043454216688639

Stories That Heal: Understanding the Effects of Creating Digital Stories With Pediatric and Adolescent/Young Adult Oncology Patients

2017· article· en· W2589066033 on OpenAlexaff
Catherine M. Laing, Nancy J. Moules, Andrew Estefan

Bibliographic record

VenueJournal of Pediatric Oncology Nursing · 2017
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPediatric oncologyPsychosocialDigital storytellingMedicineStorytellingChildhood cancerClinical PracticePsychologyPsychotherapistCancerNarrativeNursingInternal medicinePedagogy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.355
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations59
Published2017
Admission routes1
Has abstractyes

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