Perceptions of the 2019 Canada’s Food Guide: a qualitative study with parents from Southwestern Ontario
Bibliographic record
Abstract
In January 2019, Health Canada released a revised Canada’s Food Guide (CFG). This study aimed to understand the perceptions of the 2019 CFG among Canadian parents with children aged 2–12 years. From October 2019–January 2020, 8 focus groups with parents from Southwestern Ontario were conducted using a semi-structured interview script focused on understanding perceptions of the 2019 CFG. A hybrid thematic approach with inductive and deductive analyses was used. Forty parents (72.5% mothers, 77.5% white) with diverse levels of education and income participated. Most parents were aware of the 2019 CFG. Positive perceptions of the 2019 CFG were that the guide was visually appealing and less biased from the food industry compared with previous guides. Parents also reported that the focus on eating behaviours could support healthy eating among their families. Negative perceptions included insufficient information about plant-based proteins, removal of milk and alternatives food group, and lack of representation of various cultures in the guide. Challenges to following the CFG recommendations, including time constraints and limited knowledge regarding how to identify and prepare plant-based proteins, were discussed. Parent perceptions of the CFG can help inform public health policies and behavioural-change strategies designed to support adherence to the 2019 CFG recommendations. Novelty: Awareness of the 2019 Canada’s Food Guide (CFG) was high. Parents identified that the 2019 CFG was visually appealing and less biased from the food industry. Concerns about the recommendations of plant-based proteins, “exclusion” of dairies, and lack of cultural representation.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".