Food and beverage manufacturing and retailing company policies and commitments to improve the healthfulness of Canadian food environments
Bibliographic record
Abstract
BACKGROUND: Food and beverage companies play a central role in shaping the healthfulness of food environments. METHODS: The BIA-Obesity tool was used to evaluate and benchmark the specificity, comprehensiveness and transparency of the food environment-related policies and commitments of leading food and beverage manufacturing and retailing companies in Canada. Policies and commitments related to the healthfulness of food environments within 6 action areas were assessed: 1) corporate nutrition strategy; 2) product (re)formulation; 3) nutrition information and labelling; 4) product and brand promotion; 5) product accessibility; and 6) disclosure of relationships with external organizations. Data were collected from publicly available sources, and companies were invited to supplement and validate information collected by the research team. Each company was then assigned a score out of 100 for each action area, and an overall BIA-Obesity score out of 100. RESULTS: Overall BIA-Obesity scores for manufacturers ranged from 18 to 75 out of 100 (median = 49), while scores for retailers ranged from 21 to 25 (median = 22). Scores were highest within the product (re)formulation (median = 60) followed by the corporate nutrition strategy (median = 59) domain for manufacturers, while retailers performed best within the corporate nutrition strategy (median = 53), followed by the disclosure of relationships with external organizations (median = 47) domain. Companies within both sectors performed worst within the product accessibility domain (medians = 8 and 0 for manufacturers and retailers, respectively). CONCLUSIONS: This study highlights important limitations to self-regulatory approaches of the food and beverage industry to improve the healthfulness of food environments. Although some companies had specific, comprehensive, and transparent policies and commitments to address the healthfulness of food environments in Canada, most fell short of recommended best-practice. Additional mandatory government policies and regulations may be warranted to effectively transform Canadian food environments to promote healthier diets and prevent related non-communicable diseases.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".