Abstract 16687: The Use of Evidence by Stakeholders in Developing Nutrition Policy: Preventing Heart Disease and Stroke Through Reducing Sodium and Trans Fat Intake
Bibliographic record
Abstract
Introduction: High intake of sodium and trans fat are leading risk factors for heart disease and stroke. Scientific bodies including AHA have recommend reductions in their intake to prevent poor health outcomes. The government of Canada developed sodium and trans fat reduction strategies in collaboration with a wide range of stakeholders. Both strategies resulted in a voluntary approach despite the efforts of the cardiovascular and public health communities to secure regulation. Methods: A mixed method research study was undertaken to assess and analyze the policy making process around trans fat and sodium reduction. A novel content analysis was conducted on the Government of Canada lobby registry which quantified the lobbying efforts of various stakeholders. Next, key informant interviews were conducted with stakeholders involved in the policy processes to better understand contextual dynamics that led to the adoption of voluntary measures. Results: The lobby registry assessment found that the during the policy negotiations, the food industry was significantly more likely to meet with government officials than public health and clinical associations. The food industry also met with higher profile and powerful government officials than health organizations which may have facilitated a greater influence. Based on thematic analysis, the use of scientific evidence and values played a key role in the decision-making processes and the overall effectiveness of the voluntary policy measures. Data and evidence were misused by stakeholders, and this could have contributed to a general confusion about the need for public health policy and regulation. Conclusions: The policy processes around trans fat and sodium reduction were influenced by competing commercial interests which overtook the objectives of public health and clinical associations. As health groups aim to create healthier food environments around the world, it is important to understand and be prepared for the various interpretations of evidence, policy objections and counter tactics of powerful multi-national food companies. Efforts should also be made to strategically align stakeholders who influence public health policy including media, the general public and health professionals.
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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 | 0.149 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".