Better Lunch Boxes: Testing the Feasibility and Acceptability of a Family-Based Pilot Intervention to Support Nutritious Home-Packed Lunches
Bibliographic record
Abstract
Background/Objectives: The majority of Canadian children bring a home-packed lunch to school, and previous research suggests lunches are of poor nutritional quality. This pilot study aimed to test the feasibility, acceptability, and preliminary impact of an eHealth family-based intervention designed to improve the nutritional quality of home-packed lunches. Methods: In this 12-week intervention, families (n = 20 parents with children aged 4–8 years) received a toolkit which included a cookbook on tips for preparing healthy lunches and 15 tested lunch box-friendly recipes, a lunch box, text messages, and an online cooking class. Feasibility was assessed via documentation of intervention delivery and participant retention rates. Acceptability was assessed via post-intervention surveys and semi-structured interviews in a sub-sample of parents (n = 9). Preliminary impact was assessed using 3-day lunch food records. Descriptive statistics were used to assess feasibility and acceptability, and Wilcoxon signed-rank tests were used to evaluate changes in the nutritional content of packed lunches. Results: Findings indicated a high retention rate (85%), and the majority (94%) of participants reported that the intervention was helpful and that they would recommend it to another parent. Qualitative interviews suggest parents found the recipes practical and diverse, the lunch box and the cooking class helpful, and some reported increased confidence and greater awareness of the foods being packed. No changes in the nutritional content of packed lunches were observed (n = 10 children). Conclusions: In summary, a home-packed lunchbox intervention is feasible and well accepted by families, but further refinements are needed to optimize its impact before a full-scale trial.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".