Development and pilot testing of the Nutrition Attitudes and Knowledge Questionnaire to measure changes of child nutrition knowledge related to the Canada’s Food Guide
Bibliographic record
Abstract
Foodbot Factory is a serious game developed to teach children about the 2019 Canada’s Food Guide (CFG) healthy eating principles. Because no measurement tools existed to assess changes in children’s knowledge of the CFG, the Nutrition Attitudes and Knowledge (NAK) questionnaire was developed for this purpose. The NAK is based on the 2019 CFG nutrition content and aligned with the Foodbot Factory modules (Drinks, Whole Grain foods, Vegetables and Fruit, Protein foods). Seven experts assessed face and content validity of the draft NAK questionnaire. Three sections were deemed valid, while the remaining 2 required minor revisions. The NAK was pilot tested for changes in nutrition attitudes and knowledge among children aged 9–10 years-old (n = 23), who answered the NAK questionnaire before and after using Foodbot Factory. Significant increases were found in overall nutrition knowledge, and knowledge of Whole Grain foods, Vegetables and Fruit and Protein foods. Knowledge of Drinks and nutrition attitudes remained unchanged. The NAK showed a moderate reliability when tested among a group of children (n = 23). While the NAK questionnaire is a promising tool for assessing changes nutrition knowledge related to the 2019 CFG guidelines in children, further research is required to test construct validity of this instrument. Novelty: The Nutrition Attitudes and Knowledge (NAK) questionnaire was developed by educators and dietitians. The NAK underwent face and content validity assessments and was pilot tested among children. The NAK questionnaire is a potential tool to detect changes in children’s knowledge of the 2019 Canada’s Food Guide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".