Use of the GRADE approach in health policymaking and evaluation: a scoping review of nutrition and physical activity policies
Bibliographic record
Abstract
BACKGROUND: Nutrition and physical activity policies have the potential to influence lifestyle patterns and reduce the burden of non-communicable diseases. In the world of health-related guidelines, GRADE (Grading of Recommendations, Assessment, Development and Evaluation) is the most widely used approach for assessing the certainty of evidence and determining the strength of recommendations. Thus, it is relevant to explore its usefulness also in the process of nutrition and physical activity policymaking and evaluation. The purpose of this scoping review was (i) to generate an exemplary overview of documents using the GRADE approach in the process of nutrition and physical activity policymaking and evaluation, (ii) to find out how the GRADE approach has been applied, and (iii) to explore which facilitators of and barriers to the use of GRADE have been described on the basis of the identified documents. The overarching aim of this work is to work towards improving the process of evidence-informed policymaking in the areas of dietary behavior, physical activity, and sedentary behavior. METHODS: A scoping review was conducted according to current reporting standards. MEDLINE via Ovid, the Cochrane Library, and Web of Science were systematically searched up until 4 July 2019. Documents describing a body of evidence which was assessed for the development or evaluation of a policy, including documents labeled as "guidelines," or systematic reviews used to inform policymaking were included. RESULTS: Thirty-six documents were included. Overall, 313 GRADE certainty of evidence ratings were identified in systematic reviews and guidelines; the strength of recommendations/policies was assessed in four documents, and six documents mentioned facilitators or barriers for the use of GRADE. The major reported barrier was the initial low starting level of a body of evidence from non-randomized studies when assessing the certainty of evidence. CONCLUSION: This scoping review found that the GRADE approach has been used for policy evaluations, in the evaluation of the effectiveness of policy-relevant interventions (policymaking), as well as in the development of guidelines intended to guide policymaking. Several areas for future research were identified to explore the use of GRADE in health policymaking and evaluation.
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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 | 0.312 | 0.615 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.074 | 0.049 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".