Examining the nutritional quality of the product portfolios of major packaged food and beverage companies in Canada
Bibliographic record
Abstract
Abstract Canada's food supply is abundant in energy-dense products containing excess amounts of sodium, saturated fat and free sugars, increasing Canadians' risk of developing obesity and non-communicable diseases. Food companies shape the food supply through their control over the formulation of their products; however, no studies have examined the healthfulness of products offered by different companies in Canada. This study aimed to assess and compare the nutritional quality of the product portfolios of major packaged food and beverage companies in Canada. Twenty-two top food companies were selected for study, representing a combined 50% and 73% of Canadian packaged food and beverage sales in 2018, respectively. This included 18 multinational companies, 2 Canadian manufacturers and 2 retailers with private-label brands. Nutritional information for products was sourced from the University of Toronto Food Label Information Program 2017 database. The nutritional quality of all products offered by the sampled companies that were included in the database (n = 8,211) were evaluated using the Health Star Rating (HSR) system, with HSRs ranging from 0.5 (less healthy) to 5 (healthier). Descriptive analyses and analysis of variance with post-hoc tests examined the HSRs of each company's products overall and by food category (n = 24). Mean HSRs of companies’ overall product portfolios ranged from 1.8 to 3.7 (μ x̅ = 2.7, σ x̅ = 0.5) and differed significantly between companies ( p < 0.001). Mean HSRs differed between companies for all food categories except eggs ( p = 0.5), seafood ( p = 0.2), legumes ( p = 0.1), nuts and seeds ( p = 0.4), and vegetables ( p = 0.08). Variation in mean HSRs of products offered by different companies was greatest for beverages (range = 1.3–5.0, μ x̅ = 2.0, σ x̅ = 1.0), fats/oils (range = 0.7–4.4, μ x̅ = 3.6, σ x̅ = 1.6), fruit/fruit juices (range = 0.8–4.0, μ x̅ = 2.6, σ x̅ = 0.9), and sauces/dips/gravies/condiments (range = 0.5–3.4, μ x̅ = 2.3, σ x̅ = 1.0). These findings demonstrate that the nutritional quality of products offered by leading food manufacturers in Canada varies significantly overall and by food category, with many of these products considered less healthy according to the HSR system. Differences between companies may reflect the nature of their products; for example, products offered by dairy companies were healthier than those of confectionary and soft drink manufacturers, on average. Variation in nutritional quality within food categories illustrates the need and potential for many companies to improve the healthfulness of their products. By identifying companies that offer less healthy products compared with others in Canada, this study may prompt reformulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".