The effectiveness of food system policies to improve nutrition, nutrition-related inequalities and environmental sustainability: a scoping review
Bibliographic record
Abstract
Abstract A global transformation of food systems is needed, given their impact on the three interconnected pandemics of undernutrition, obesity and climate change. A scoping review was conducted to synthesise the effectiveness of food system policies/interventions to improve nutrition, nutrition inequalities and environmental sustainability, and to identify double- or triple-duty potentials (their effectiveness tackling simultaneously two or all of these outcomes). When available, their effects on nutritional vulnerabilities and women’s empowerment were described. The policies/interventions studied were derived from a compilation of international recommendations. The literature search was conducted according to the PRISMA extension for scoping reviews. A total of 196 reviews were included in the analysis. The triple-duty interventions identified were sustainable agriculture practices and school food programmes. Labelling, reformulation, in-store nudging interventions and fiscal measures showed double-duty potential across outcomes. Labelling also incentivises food reformulation by the industry. Some interventions (i.e., school food programmes, reformulation, fiscal measures) reduce socio-economic differences in diets, whereas labelling may be more effective among women and higher socio-economic groups. A trade-off identified was that healthy food provision interventions may increase food waste. Overall, multi-component interventions were found to be the most effective to improve nutrition and inequalities. Policies combining nutrition and environmental sustainability objectives are few and mainly of the information type (i.e., labelling). Little evidence is available on the policies/interventions’ effect on environmental sustainability and women’s empowerment. Current research fails to provide good-quality evidence on food systems policies/interventions, in particular in the food supply chains domain. Research to fill this knowledge gap is needed.
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 | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".