Validation of an Asthma Knowledge Tool to Assess the Efficacy of Comprehensive Asthma Education
Bibliographic record
Abstract
INTRODUCTION: Asthma is a leading cause of chronic disease in children, and poor control is often associated with a lack of asthma knowledge, which can be improved through education. Thus, it is important to have a validated tool that measures asthma knowledge. We aimed to develop an asthma knowledge tool to assess the efficacy of comprehensive asthma education for caregivers of children with asthma. METHODS: An asthma knowledge tool was designed using existing questionnaires and pilot tested amongst 10 asthma experts and 20 parents who had previously received asthma education. The Children's Hospital of Eastern Ontario Asthma Knowledge Tool (CHAT) consists of 35 true/false questions. The CHAT was then prospectively administered to caregivers between June 2021 and 2023 before receiving education, < 7 days following education, and < 2 weeks after completing posttest 1. Internal responsiveness of the CHAT was measured by comparing pre- and posttest scores using paired t-tests and determining effect size. Test-retest reliability was calculated using the two-way mixed effect Intraclass correlation coefficient (ICC) for posttest scores. RESULTS: Mean CHAT scores amongst 129 participants increased by 6.2 points between pretest to posttest 1 (95% CI 5.4-7.0), with an estimated effect size of 1.2, indicating a large change (p < 0.001). Test-retest reliability was "good" to "excellent" with an estimated ICC of 0.85 between posttests. CONCLUSION: The CHAT exhibited good discrimination, and reliably measured a significant improvement in knowledge scores following asthma education. The CHAT is a validated asthma knowledge tool for caregivers of children with asthma, having implications for future quality improvement and research studies.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".