Monitoring policy and actions on food environments: rationale and outline of the INFORMAS policy engagement and communication stategies
Bibliographic record
Abstract
The International Network for Food and Obesity/non-communicable \ndiseases Research, Monitoring and Action Support (INFORMAS) proposes \nto collect performance indicators on food policies, actions and \nenvironments related to obesity and non-communicable diseases. This \npaper reviews existing communications strategies used for performance \nindicators and proposes the approach to be taken for INFORMAS. \nTwenty-seven scoring and rating tools were identified in various fields \nof public health including alcohol, tobacco, physical activity, infant \nfeeding and food environments. These were compared based on the \ntypes of indicators used and how they were quantified, scoring methods, \npresentation and the communication and reporting strategies used. \nThere are several implications of these analyses for INFORMAS: the \nratings/benchmarking approach is very commonly used, presumably \nbecause it is an effective way to communicate progress and stimulate \naction, although this has not been formally evaluated; the tools used \nmust be trustworthy, pragmatic and policy-relevant; multiple channels \nof communication will be needed; communications need to be tailored \nand targeted to decision-makers; data and methods should be freely \naccessible. The proposed communications strategy for INFORMAS has \nbeen built around these lessons to ensure that INFORMAS’s outputs \nhave the greatest chance of being used to improve 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 | 0.199 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".