Ultra-processed Foods Frequently Promoted in Canada’s Grocery Flyers from 2014, 2017, and 2021, with Healthier Cover Pages
Bibliographic record
Abstract
Purpose: To assess nutritional quality of foods promoted in Canadian grocery flyers including level of processing, and whether adherence to dietary guidance varied by year, region, store type (regular or discount), or location in the flyer. Methods: Observational study; national sample of weekly digitized flyers (n = 53 flyers; n = 8,790 promoted foods and beverages) were collected from Canada’s largest market-share national grocer, from 2014, 2017, and 2021. Flyer food items were extracted and coded manually into food groups and categories. Nutritional quality of promoted items was analyzed by three nutrient profiling systems including NOVA level of processing and Canada’s Food Guide (CFG). Logistic regression models were used to examine odds of flyer foods aligning with dietary guidance. Results: Mean food items per flyer were 106 (SD = 66.4) in 2014; 174 (SD = 106.2) in 2017; and 215 (SD = 49.8) in 2021. Baked products (11%), non-alcoholic beverages (10.7%) and milk/dairy (10.2%) comprised the largest proportions of flyers. Least healthy/ultra-processed foods were most frequently advertised (CFG: 58.5%; NOVA: 51.2%); but followed by the most healthy/un/minimally processed (CFG: 33.6%: NOVA: 27.8%). Across geographic regions, odds of CFG alignment were similar, and no different in discount versus regular banners. Food items on the flyer cover page had consistently higher odds of CFG alignment. Conclusion: Consistent with past research, flyer foods frequently did not align with dietary guidance.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".