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Record W2771856202 · doi:10.2196/jmir.8877

Development and Usability Evaluation of an Art and Narrative-Based Knowledge Translation Tool for Parents With a Child With Pediatric Chronic Pain: Multi-Method Study

2017· article· en· W2771856202 on OpenAlexafffund
Kathy Reid, Lisa Hartling, Samina Ali, Anne Le, Allison Norris, Shannon D. Scott

Bibliographic record

VenueJournal of Medical Internet Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsStollery Children's HospitalWomen and Children’s Health Research InstituteUniversity of AlbertaAlberta Health Services
FundersChildren's Hospital FoundationStollery Children’s Hospital FoundationWomen and Children's Health Research InstituteChildren's Health Research Institute
KeywordsUsabilityNarrativeChronic painKnowledge translationQualitative researchPsychologyMedicineMedical educationComputer scienceKnowledge managementPsychiatrySociology

Abstract

fetched live from OpenAlex

Background: Chronic pain in childhood is increasingly being recognized as a significant clinical problem for children and their families. Previous research has identified that families want information about the causes of their child’s chronic pain, treatment options, and effective strategies to help their child cope with the pain. Unfortunately, parents have reported that finding this information can be challenging. Objective: The aim of this study was to actively work together with children attending a pediatric chronic pain clinic and their parents to develop, refine, and evaluate the usability of an art and narrative-based electronic book (e-book) for pediatric chronic pain. Methods: A multiphase, multi-method research design employing patient engagement techniques was used to develop, refine, and evaluate the usability of an art and narrative based e-book for pediatric chronic pain management to facilitate knowledge translation for parents with a child with chronic pain. The multiple phases included the following: (1) qualitative interviews to compile parents’ narratives using qualitative interviews; (2) qualitative data analysis; (3) development of an e-book prototype; (4) expert clinician feedback; (5) parent usability evaluation, knowledge change, and confidence in knowledge responses using an electronic survey; (6) e-book refinement; and (7) dissemination of the e-book. Results: A 48-page e-book was developed to characterize the experiences of a family living with a child with chronic pain. The e-book was a composite narrative of the parent interviews and encompassed descriptions of the effects the condition has on each member of the family. This was merged with the best available research evidence on the day-to-day management of pediatric chronic pain. The e-book was vetted for clinical accuracy by expert pediatric pain clinicians. All parents that participated in the usability evaluation (N=14) agreed or strongly agreed the content of the e-book was easy to understand and stated that they would recommend the e-book to other families who have children with chronic pain. Our research identified up to a 21.4% increase in knowledge after using the e-book, and paired t tests demonstrated a statistically significant difference in confidence in answering two of the five knowledge questions (chronic pain is a disease involving changes in the nervous system; the use of ibuprofen is usually effective at controlling chronic pain); t13=0.165, P=.001 and t13=0.336, P=.002, respectively, after being exposed to the e-book. Conclusions: Our results demonstrate that parents positively rated an e-book developed for parents with a child with chronic pain. Our results also identify that overall, parents’ knowledge increased after using the e-book, and confidence in their knowledge about chronic pain and its management increased in two aspects after e-book exposure. These results suggest that art and narrative-based knowledge translation interventions may be useful in transferring complex health information to parents.

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)0.089
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0890.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.339
GPT teacher head0.613
Teacher spread0.274 · 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.

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

Citations45
Published2017
Admission routes2
Has abstractyes

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