Assessing the Feasibility and Acceptability of a Virtual Food Skills and Food Sustainability Program Designed for Children Living With Type 1 Diabetes
Bibliographic record
Abstract
Objective: To assess the feasibility and acceptability of a virtual food skills program for children with type 1 diabetes. Methods: Forty‐three patients, aged 6–14 years with type 1 diabetes, participated in an 8‐week online programme, summerlunch+ At Home, that included weekly live cooking classes, asynchronous learning modules, and quizzes accessed through Google Classroom. Grocery delivery or gift cards were provided to all participants to support equitable access to participation. Descriptive results were summarized, and thematic analysis was performed on answers to a post‐intervention questionnaire, parent/caregivers interview transcripts, and facilitators’ field notes. Results: Participants reported having a positive experience and would recommend the programme to others. Acceptable elements included the online format, the cooking class demonstrations, and the well‐organized content. Families enjoyed the recipes, expressed an improvement in the families’ cooking skills and nutrition knowledge, and noted the program as a way to improve family bonding and reduce participants’ sense of social isolation given the opportunity of meeting peers with diabetes. The intervention also appears to increase participants’ independence, confidence, and self‐esteem. While grocery cards were easier to coordinate compared with meal kits, both were deemed acceptable by caregivers. Barriers to participation include a distracting home environment and not feeling comfortable on camera. Factors that negatively impacted satisfaction were the large age range of participants and the class timing and duration. Caregivers noted a desire for more diabetes education, enhanced peer‐to‐peer interaction, and incorporation of animal‐based protein recipes in future programmes. Conclusion: The current study demonstrates the feasibility and acceptability of the virtual summerlunch+ At Home cooking and nutrition program that was adapted for children with diabetes. Similar food skills programmes may support the development of food skills imperative to diabetes self‐management long‐term. Further research can continue to assess food literacy skills, glycemic management, and the social benefits of such interventions.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".