Development of a Responsible Policy Index to Improve Statutory and Self-Regulatory Policies that Protect Children’s Diet and Health in the America’s Region
Bibliographic record
Abstract
In 2010, 193 Member States of the World Health Organization (WHO) endorsed World Health Assembly Resolution WHA63.14 to restrict the marketing of food and beverage products high in fat, sugar and salt (HFSS) to children to prevent obesity and non-communicable diseases (NCDs). No study has examined HFSS marketing policies across the WHO regional office countries in the Americas. Between 2018 and 2019, a transdisciplinary team examined policies to restrict HFSS food and beverage product marketing to children to develop a responsible policy index (RESPI) that provides a quality score based on policy characteristics and marketing techniques. After designing the RESPI, we conducted a comprehensive literature review through October 2019 to examine policies in 14 countries in the WHO Americans Region. We categorized policies (n = 38) as either self-regulatory or statutory and calculated the RESPI scores, ranked from 0 (lowest) to 10 (highest). Results showed Brazil, Canada, Chile, and Uruguay had the highest RESPI scores associated with statutory policies that restricted point of sale, cartoon, licensed media characters and celebrities; and HFSS products in schools and child care settings, and broadcast and print media. Policymakers can use the RESPI tool to evaluate marketing policies within and across geopolitical boundaries to protect children’s diet and health.
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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 | 0.034 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".