Lobbying and nutrition policy in Canada: a quantitative descriptive study on stakeholder interactions with government officials in the context of Health Canada’s Healthy Eating Strategy
Bibliographic record
Abstract
BACKGROUND: The political activities of industry stakeholders must be understood to safeguard the development and implementation of effective public health policies. METHODS: A quantitative descriptive study was performed using data from Canada's Registry of Lobbyists to examine the frequency and governmental target of lobbying that occurred between various types of stakeholders (i.e., industry versus non-industry) and designated public office holders (DPOH) regarding Health Canada's Healthy Eating Strategy, from September/2016 to January/2021. Initiatives of interest were revisions to Canada's Food Guide, changes to the nutritional quality of the food supply, front-of-pack nutrition labelling and restrictions on food marketing to children. RESULTS: The majority of registrants (88%), and corporations and organizations (90%) represented in lobbying registrations had industry ties. Industry-affiliated stakeholders were responsible for 86% of communications with DPOH, interacting more frequently with DPOH of all ranks, compared to non-industry stakeholders. Most organizations and corporations explicitly registered to lobby on the topic of marketing to children (60%), followed by Canada's Food Guide (48%), front-of-pack nutrition labelling (44%), and the nutritional quality of the food supply (23%). The food and beverage industry, particularly the dairy industry, was the most active, accounting for the greatest number of lobbying registrations and communications, followed by the media and communication industry. CONCLUSIONS: Results suggest a strategic advantage of industry stakeholders in influencing Canadian policymakers. While some safeguards have been put in place, increased transparency would allow for a better understanding of industry discourse and help protect public health interests during the policy development process.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".