The healthy food environment policy index: findings of an expert panel in New Zealand
Bibliographic record
Abstract
OBJECTIVE: To assess government actions to improve the healthiness of food environments in New Zealand, based on the healthy food environment policy index. METHODS: A panel of 52 public health experts rated the extent of government implementation against international best practice for 42 indicators of food environment policy and infrastructure support. Their ratings were informed by documented evidence, validated by government officials and international benchmarks. FINDINGS: There was a high level of implementation for some indicators: providing ingredient lists and nutrient declarations and regulating health claims on packaged foods; transparency in policy development; monitoring prevalence of noncommunicable diseases and monitoring risk factors for noncommunicable diseases. There was very little, if any implementation of the following indicators: restrictions on unhealthy food marketing to children; fiscal and food retail policies and protection of national food environments within trade agreements. Interrater reliability was 0.78 (95% confidence interval, CI: 0.76-0.79). Based on the implementation gaps, the experts recommended 34 actions, and prioritized seven of these. CONCLUSION: The healthy food environment policy index provides a useful set of indicators that can focus attention on where government action is needed. It is anticipated that this policy index will increase accountability of governments, stimulate government action and support civil society advocacy efforts.
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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 | 0.085 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".