Older Adults’ Perceptions of the 2019 Canada’s Food Guide: A Qualitative Study
Bibliographic record
Abstract
Purpose: Major changes were made to Canada’s Food Guide (CFG) in 2019. This study aimed to understand the perceptions of older adults toward this newest version. Methods: Older adults were invited to participate via newsletters sent to older adults and retirees’ organizations in the Province of Quebec. Participants completed an online survey about their baseline familiarity with the 2019 CFG using a 5-point Likert scale and took part in an individual semi-structured online interview, which explored their perceptions toward the 2019 CFG. A thematic qualitative analysis of the interview transcripts was performed. Results: Fifty-eight older adults (>65 years, 30 women, 28 men, including 19 consumers and 39 non-consumers of plant-based protein (PBP) foods) participated in the study. Older adults were mostly familiar with the 2019 CFG and had a positive perception of its features. They appreciated the design, proposed recipes, and healthy eating recommendations. Perceptions about the three food groups were mixed, mainly regarding the decreased emphasis on dairy products. Some appreciated that animal proteins were less prominent, while others raised issues on how to integrate PBP into their diet. Perceptions appeared to be influenced by sex and PBP consumption. Conclusion: Older adults in the Province of Quebec view most of the 2019 CFG recommendations positively. Our observations may be useful to dietitians and public health practitioners when developing strategies to improve adherence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".