Optimizing pediatric asthma education using virtual platforms during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: We compared patient and caregiver knowledge and confidence for managing asthma, and participant experiences when comprehensive asthma education was delivered in person versus in the virtual setting. METHODS: We performed a multi-methods study using structured surveys and qualitative interviews to solicit feedback from patients and caregivers following participation in a comprehensive asthma education session between April 2018 and October 2021. We compared participant knowledge and confidence for managing asthma as well as user experience when the education was attended in-person or virtually. Quantitative responses were summarized descriptively, and qualitative feedback was analyzed for major themes. RESULTS: Of 100 caregivers/patients who completed post education satisfaction surveys and interviews, 52 attended in person and 48 virtually, with the mean age of patients being 6.7 years (range: 1.2-17.0). Participant reported gains in knowledge and confidence for asthma management were not different between groups and 65.2% preferred attending virtual asthma education. The majority of participants described virtual education as a safer modality that was more convenient and accessible. CONCLUSIONS: We demonstrated the successful implementation of a novel, virtual asthma education program for patients and caregivers of children with asthma. Both virtual and in-person delivered asthma education were equally effective for improving perceived knowledge and confidence for asthma self-management and virtual education was considered safer, more convenient and accessible. Virtual asthma education offers an attractive and effective option for improving the reach of quality asthma education programs and may allow more children/patients to benefit.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".