Postsecondary Students’ Knowledge of and Adherence to the 2019 Canada’s Food Guide: A Cross-sectional Pilot Survey
Bibliographic record
Abstract
Purpose: We conducted a pilot survey among young adults attending a suburban Canadian university to understand: (1) knowledge of the 2019 Canada’s Food Guide (CFG); (2) self-reported food choices and eating habits; (3) perceived influence of the CFG on food choices and eating habits; and (4) suggestions to improve engagement with CFG. Methods: Students were recruited, through posts on social media platforms, to complete an online questionnaire between 7 March and 6 April 2020. Results: One-hundred and twenty-one (70% women) students responded. One-third (33%) of women and 8% of men reported consuming the recommended proportion of vegetables and fruits (i.e., 40%–60% of the plate) at their most recent meal (P = 0.001). Men were more likely to report overconsuming protein foods than women (58% vs 32%, P = 0.005). The perceived influence of the CFG on food choices and eating habits was low, with a mean score 2.2 ± 1.4 out of 7, with 7 indicating “highly influential.” Over 92% of participants believed awareness of the CFG could be improved through social media platforms. Conclusions: Although half of the participants correctly answered all 8 questions that assessed knowledge of the CFG, there is an opportunity for dietitians and related health professionals to improve engagement with CFG.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".