Using GRADE Evidence to Decision frameworks to support the process of health policy-making: an example application regarding taxation of sugar-sweetened beverages
Bibliographic record
Abstract
BACKGROUND: Grading of Recommendations, Assessment, Development and Evaluation (GRADE) Evidence to Decision (EtD) frameworks are well-known tools that enable guideline panels to structure the process of developing recommendations and making decisions in healthcare and public health. To date, they have not regularly been used for health policy-making. This article aims to illustrate the application of the GRADE EtD frameworks in the process of nutrition-related policy-making for a European country. METHODS: Based on methodological guidance by the GRADE Working Group and the findings of our recently published scoping review, we illustrate the process of moving from evidence to recommendations, by applying the EtD frameworks to a fictitious example. Sugar-sweetened beverage (SSB) taxation based on energy density was chosen as an example application. RESULTS: A fictitious guideline panel was convened by a national nutrition association to develop a population-level recommendation on SSB taxation aiming to reduce the burden of overweight and obesity. Exemplary evidence was summarized for each EtD criterion and conclusions were drawn based on all judgements made in relation to each criterion. As a result of the high priority to reduce the burden of obesity and because of the moderate desirable effects on health outcomes, but considering scarce or varying research evidence for other EtD criteria, the panel made a conditional recommendation for SSB taxation. Decision-makers may opt for conducting a pilot study prior to implementing the policy on a national level. CONCLUSIONS: GRADE EtD frameworks can be used by guideline panels to make the process of developing recommendations in the field of health policy more systematic, transparent and comprehensible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.358 | 0.548 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.016 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.021 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".