The development and usability testing of two arts-based knowledge translation tools for pediatric asthma
Bibliographic record
Abstract
Abstract Asthma is the most common chronic condition in children with an estimated 15% of children and youth living with asthma in Canada. Acute asthma exacerbations, or asthma attacks, are the main reason for children to seek emergency care, contributing to financial burdens for families and healthcare systems. This burden highlights opportunities to reduce health system costs and improve patient and family education. We worked with parents of children with asthma to develop and evaluate two digital knowledge translation (KT) tools on asthma. These tools merge the best available research evidence with narratives of parent experiences, and use art and engaging media (video and interactive infographic) to optimize uptake and appeal. Following prototype completion, usability testing was conducted among 60 parents (30 parents per tool) in an urban Alberta emergency department waiting room. Parents viewed the tools on an iPad and answered questions via an electronic survey. Usability was assessed based on nine items with responses on a five-point Likert scale from 1=strongly disagree to 5=strongly agree. Overall, results were positive and the tools were highly rated across most usability items. Mean scores across usability items were 4.13 to 4.63 for the video and 4.10 to 4.43 for the infographic. The scores from the usability testing suggest arts-based digital tools are useful in sharing complex health information with parents about the care of a child with asthma and provide meaningful guidance on how to improve KT tools to better reflect the needs of parents of children with asthma.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".