Efficacy of the Foodbot Factory digital curriculum-based nutrition education intervention in improving children’s nutrition knowledge, attitudes and behaviours in elementary school classrooms: protocol for a cluster randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Schools are an important setting for supporting children's development of food literacy, but minimal research has assessed which strategies are most suitable for school nutrition education. The Foodbot Factory intervention, consisting of serious game (ie, a digital game designed for education) and curriculum-based lesson plans, was developed to support teachers and children ages 8-12 with nutrition education. Pilot data have demonstrated that Foodbot Factory can significantly improve children's nutrition knowledge, but it has not yet been evaluated in classrooms. METHODS AND ANALYSIS: A single-blinded cluster randomised controlled trial was designed in 2022 by a research team based at Ontario Tech University to determine the efficacy of the Foodbot Factory intervention in improving children's nutrition knowledge, attitudes and behaviours. 32 grade 4 and 4/5 classrooms in Ontario will be randomised to receive (1) the Foodbot Factory intervention or (2) a control nutrition education intervention using conventional materials (eg, activity sheets). The study's primary outcome is to determine the overall nutrition knowledge acquired from the intervention. Secondary outcomes include nutrition knowledge subscores (ie, knowledge of specific food groups), nutrition attitudes, dietary intake, general nutrition behaviours (eg, eating breakfast) and intervention acceptability. An Ontario-certified teacher will deliver the intervention to both groups for 35-40 min/day for five consecutive days. Outcomes will be assessed at baseline, immediately postintervention, and 4 weeks and 3 months postintervention using the Nutrition Attitudes and Knowledge questionnaire, the Block Kids Food Screener, a modified Family Nutrition and Physical Activity screener and an acceptability questionnaire. Generalised linear mixed models will assess changes in outcomes between groups. ETHICS AND DISSEMINATION: The study protocol is approved by research ethics boards at Ontario Tech University and participating school boards. Results of the trial will be published in peer-reviewed journals and lay summaries will be available to stakeholders. TRIAL REGISTRATION NUMBER: NCT05979259.
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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 | 0.034 | 0.027 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.010 |
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".