Online education for gluten‐free diet teaching: Development and usability testing of an e‐learning module for children with concurrent celiac disease and type 1 diabetes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Celiac disease (CD), the most common genetically-based food intolerance, affects 3% to 16% of children with type 1 diabetes (T1D). Treatment involves lifelong adherence to a gluten-free diet (GFD). Individualized dietary education is resource-intensive. We, therefore, sought to develop and test the usability of an e-learning module aimed at educating patients and caregivers regarding implementation of the GFD in children with concurrent CD and T1D. METHODS: An interactive e-learning module was developed based on extensive review of CD, T1D, and educational literature. A mixed-methods usability testing approach was used to refine and evaluate the module, using qualitative semi-structured interviews, observations, and satisfaction and knowledge questionnaires in two iterative cycles. The module was refined based on themes identified from each usability cycle. RESULTS: Eighteen patients (8 in cycle 1, 10 in cycle 2) and 15 caregivers (7 in cycle 1, 8 in cycle 2) participated. Patient participants had CD and T1D for a mean (SD) of 6.1 ± 5.1 and 8.3 ± 5.5 years, respectively. Their mean age was 13.5 ± 4.5 years. Thematic analysis of usability interviews showed the module to be appealing and resulted in minor module revisions after each cycle to improve usability. Mean satisfaction scores post-module completion were high (4.67 ± 0.54), indicating participants were "very satisfied" with the education. Knowledge test scores increased significantly from pre- to post-module completion (P = 0.001). CONCLUSION: A multifaceted user-centered usability approach demonstrated that an innovative, interactive e-learning module is effective in knowledge retention and can provide comprehensive and accessible information in the implementation of the GFD teaching in children with CD and T1D.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".