Knowledge and perceptions of the 2019 Canada's Food Guide: a qualitative study with Canadian children
Bibliographic record
Abstract
To support Canadians ages 2 years and older in improving their dietary intake, Health Canada released a revised Canada's Food Guide (CFG) in 2019. This study aimed to explore the knowledge and perceptions of the 2019 CFG among children ages 9-12 years old from Southwestern Ontario. From September-November 2021, interviews were conducted with children by video conference. Thirty-five children (50% girls, 80% White; mean age 9.9 years) participated. Data were analyzed using a hybrid thematic approach with inductive and deductive analyses. Children expressed a lack of knowledge on certain foods (i.e., plant-based proteins, whole grains, and highly processed foods) that are highlighted in the CFG. Children also expressed confusion around food groups, including recommended proportions and categorization of some foods (e.g., dairy products and plant-based proteins). Children generally expressed positive perceptions regarding CFG and its eating habit recommendations, i.e., "Cook more often", "Eat meals with others", and "Enjoy your food", and they suggested strategies to improve adherence to these recommendations, including providing children more responsibility and independence with food preparation tasks and minimizing family time conflicts. Children's perceptions of the CFG can help inform public health policies and programmatic strategies designed to support children's food choices and eating habits.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".