Strategies used by the Canadian food and beverage industry to influence food and nutrition policies
Bibliographic record
Abstract
Abstract Background Unhealthy food environments contribute to the rising rates of obesity and diet-related diseases. To improve the Canadian nutritional landscape, Health Canada launched the Healthy Eating Strategy in October 2016 which involved several initiatives including the restriction of unhealthy food marketing to children, the reduction of sodium in the food supply and the introduction of front-of-package labelling. Subsequently, various stakeholders engaged in discussions with Health Canada. This study sought to describe the interactions between Health Canada and industry and non-industry stakeholders and to identify the strategies used by industry to influence food and nutrition policy in Canada. Methods Documents such as correspondences and presentations exchanged in interactions between Health Canada and stakeholders regarding the Healthy Eating Strategy were obtained from Health Canada’s Openness and Transparency website. The participating stakeholders of each interaction and the topics discussed were determined and described quantitatively. A directed content analysis was then conducted to identify the strategies employed by industry to influence policy. This was guided by a previously developed coding framework that was adapted during analysis. Results A total of 208 interactions concerning the Healthy Eating Strategy occurred between October 2016 and June 2018. Of the interactions for which documents were received (n = 202), 56% involved industry stakeholders, 42% involved non-industry stakeholders and 2% involved both. Industry stakeholders were more likely to initiate interactions with Health Canada (94% of their interactions) than non-industry stakeholders (49%). Front-of-package labelling was the most frequently discussed topic by industry stakeholders (discussed in 49% interactions involving industry) while non-industry stakeholders most frequently discussed the Healthy Eating Strategy as a whole (discussed in 37% of interactions involving non-industry). A wide variety of strategies were used by industry in their attempts to influence policy. Those most frequently identified included: “framing the debate on diet- and public health-related issues”, “promoting deregulation”, “shaping the evidence base”, “stressing the economic importance of industry”, and “developing and promoting alternatives to proposed policies”. Conclusion Industry stakeholders are highly active in their attempts to influence Canadian nutritional policies. Policymakers and public health advocates should be aware of these strategies so that balanced and effective food and nutrition policies can be developed.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".