Pilot test of the Healthy Food Environment Policy Index (Food-EPI) to increase government actions for creating healthy food environments
Bibliographic record
Abstract
OBJECTIVES: Effective government policies are essential to increase the healthiness of food environments. The International Network for Food and Obesity/non-communicable diseases (NCDs) Research, Monitoring and Action Support (INFORMAS) has developed a monitoring tool (the Healthy Food Environment Policy Index (Food-EPI)) and process to rate government policies to create healthy food environments against international best practice. The aims of this study were to pilot test the Food-EPI, and revise the tool and process for international implementation. SETTING: New Zealand. PARTICIPANTS: Thirty-nine informed, independent public health experts and non-governmental organisation (NGO) representatives. PRIMARY AND SECONDARY OUTCOME MEASURES: Evidence on the extent of government implementation of different policies on food environments and infrastructure support was collected in New Zealand and validated with government officials. Two whole-day workshops were convened of public health experts and NGO representatives who rated performance of their government for seven policy and seven infrastructure support domains against international best practice. In addition, the raters evaluated the level of difficulty of rating, and appropriateness and completeness of the evidence presented for each indicator. RESULTS: Inter-rater reliability was 0.85 (95% CI 0.81 to 0.88; Gwet's AC2) using quadratic weights, and increased to 0.89 (95% CI 0.85 to 0.92) after deletion of the problematic indicators. Based on raters' assessments and comments, major changes to the Food-EPI tool include strengthening the leadership domain, removing the workforce development domain, a stronger focus on equity, and adding community-based programmes and government funding for research on obesity and diet-related NCD prevention, as good practice indicators. CONCLUSIONS: The resulting tool and process will be promoted and offered to countries of varying size and income globally. International benchmarking of the extent of government policy implementation on food environments has the potential to catalyse greater government action to reduce obesity and NCDs, and increase civil society's capacity to advocate for healthy food environments.
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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 | 0.070 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".