Teaching Families of Children with Celiac Disease about Gluten-Free Diet Using Distributed Education: a Pilot Study
Bibliographic record
Abstract
Introduction: Treatment of celiac disease is a strict life-long gluten-free diet (GFD). The GFD is complex, and counseling by a dietitian is essential. The number of new referrals for GFD education has increased. We studied the feasibility of GFD teaching using distributed education. Methods: The IWK Health Center in Halifax is the only tertiary-care pediatric hospital in the 3 Maritime provinces with GFD experienced dietitians. Families travel long distances to attend teaching sessions. Families outside the Halifax area were offered to participate in the 2.5-hour education sessions held once a month via live videoconference link at their regional hospitals. All participants were surveyed with a 10-item questionnaire assessing the content and delivery and usefulness of information. Results: Over a 6-month period, 39 families attended the sessions, 21 locally and 18 at distributed sites across the Maritimes. The survey was completed by 26 participants (67%). All participants at both sites strongly agreed or agreed that their setting was good for learning and the information provided was easy to understand. There were no significant differences between the 2 groups on any individual questions in the 2 domains assessed (all P > 0.06). Conclusions: Distributed education on GFD is feasible and as effective as in person education. It affords convenience and savings to families by reducing travel costs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.004 | 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".