Perceptions and use of Canada's Food Guide to Healthy Eating by grocery shoppers in London, Ontario.
Bibliographic record
Abstract
Limited information exists on grocery shoppers' perceptions and use of Canada's Food Guide to Healthy Eating. The main objective of this study was to examine grocery shoppers' perceptions and use of the food guide in London, Ontario. The guide tearsheet and a self-administered questionnaire about food-buying practices were distributed to 2,000 food shoppers in ten London supermarkets. The response rate was 572 of 2,000 shoppers, or 29%; detailed results are reported in a separate paper. Four months later, a follow-up survey on the perceptions and use of the food guide was conducted through the use of a mailed questionnaire. (Both questionnaires had been pretested.) The response rate to the follow-up survey was 21% (118 of 572 participants). This survey revealed that 79% found the tearsheet useful or very useful. A majority indicated they would recommend its use to others. Over 75% reported awareness of the messages and almost two-thirds indicated that they had made some changes in their eating habits. About 40% provided helpful suggestions for revisions of the guide. The information obtained from this study will help health educators better understand shoppers' perceptions about the usefulness of Canada's Food Guide to Healthy Eating. The guide must be made more accessible, and shoppers' concerns about healthy food choices must be addressed.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".