Nutrition marketing on processed food packages in Canada: 2010 Food Label Information Program
Bibliographic record
Abstract
The current study describes the frequency of use of different forms of nutrition marketing in Canada and the nutrients and conditions that are the focus of nutrition marketing messages. Prepackaged foods with a Nutrition Facts table (N = 10,487) were collected between March 2010 and April 2011 from outlets of the 3 largest grocery chains in Canada and 1 major western Canadian grocery retailer. The nutrition marketing information collected included nutrient content claims, disease risk reduction claims, and front-of-pack nutrition rating systems (FOPS). We found that nutrition marketing was present on 48.1% of Canadian food packages, with nutrient content claims being the most common information (45.5%), followed by FOPS on 18.9% of packages. Disease risk reduction claims were made least frequently (1.7%). The marketing messages used most often related to total fat and trans fat (15.6% and 15.5% of nutrient content claims, respectively). Limiting total and trans fats is a current public health priority, as recommended by Health Canada and the World Health Organization. However, other nutrients that are also recommended to be limited, including saturated fats, sodium, and added sugars, were not nearly as prominent on food labels. Thus, greater emphasis should be placed by the food industry on these other important nutrients. Repeated data collection in the coming years will allow us to track longitudinal changes in nutrition marketing messages over time as food marketing, public health, and consumer priorities evolve.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".