Pediatric emergency department-based asthma education tools and parent/child asthma knowledge
Bibliographic record
Abstract
Asthma exacerbations are a leading cause of pediatric hospitalizations despite multiple efforts to educate patients and families on disease course and medication management. Asthma education in the pediatric emergency department (ED) is challenging, and although the use of written action plans has been associated with reduction in hospitalizations and ED visits, written tools may not be useful for individuals with low health literacy. Moreover, asthmatic children should participate in their asthma education. In this prospective randomized study of 53 families presenting to a pediatric ED with a child experiencing an asthma exacerbation, education on asthma was presented via an interactive mobile-based video-game versus a standard-of-care asthma education video (SAV). Median age was 10 years; 64% were males. Many patients had moderate-to-severe asthma, with 57% experiencing ≥ 2 asthma-related ED visits in the last year, 58% requiring hospitalization and 32% reporting a critical care admission. In this cohort, the mobile-based video-game was found to be a feasible, acceptable educational tool; 86% of parents and 96% of children liked the game, while 96% of parents and 76% of children preferred playing the game over watching a SAV. Despite a history of persistent asthma, only 34% of children used an inhaled corticosteroid while 70% required rescue inhaler use in the prior week. Basic asthma knowledge was sub-optimal with only 60% of parents and 43% of children correctly recognizing symptoms that should prompt immediate medical care. This reflects a major gap in asthma knowledge that coexists with parental misconceptions regarding optimal asthma management.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".