Food-based indices for the assessment of nutritive value and environmental impact of meals and diets: A systematic review protocol
Bibliographic record
Abstract
Current food production and consumption practices are impacting both human and planetary health. Though these challenges are multifaceted, shifting to healthy dietary choices from sustainable food systems is one solution. Food-based labelling is a common public-health strategy aimed at influencing consumption practices, primarily displaying front-of-pack nutrition labelling to encourage healthier choices. Due to the dual impact of food on human and planetary health there is a growing need to additionally include environmental impact information. However, this potentially conflicting information could overwhelm consumers, necessitating a simplified approach that combines both nutritive and environmental values. Previous work has identified existing models, termed sustainable food profiling models, for labelling of individual food products based on their environmental impacts and nutritive value. Foods are rarely eaten in isolation and are often consumed as composite meals, which aggregate to diets. Therefore, it is important to identify indices that exist to assess or rank meals and diets according to their nutritional and environmental impacts. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 guidelines, our systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on 01 May 2024 (PROSPERO registration ID = CRD42024537149). In the present protocol the methodology to identify and review existing food-based indices for the assessment of nutritive value and environmental impact of meals and diets and their intended public health purpose is described. Our primary research questions are: (i) what food-based indices assessing nutritive value and environmental impact exist for classification or ranking of meals or diets? and (ii) what are the methods used to create, and the key characteristics of, these indices?. The results are expected to increase understanding of, and highlight the variation in, the creation of combined measures for the assessment of nutritive value and environmental impact for meals and diets. Additionally, findings can be used to inform researchers, business, and policy actors on future approaches for labelling foods, meals, or diets in a way that supports healthy and sustainable meal choices and diets.
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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 | 0.096 | 0.137 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.083 | 0.013 |
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".