Menu labeling and portion size control to improve the out‐of‐home food environment: A scoping review
Bibliographic record
Abstract
Background: Menu labeling and portion size control interventions may be effective strategies to mitigate the health risks posed by the out-of-home food environment. We conducted this scoping review to map the body of evidence (BoE) addressing the effects of menu labeling and portion size control interventions in the out-of-home food environment and to summarize the research gaps in this evidence base. Methods: We searched PubMed, Embase, Epistemonikos, and PROSPERO in phase 1 for systematic reviews (SRs) and PubMed and Embase in phase 2 for primary studies in areas with insufficient SR evidence. We used a comprehensive search strategy without any restrictions on publication date, language, study population characteristics or outcomes. We screened all titles independently and in duplicate. We mapped the number of systematic reviews providing evidence per intervention-setting combination in a matrix. The gaps in the matrix informed the searches for primary studies in phase 2. For the included SR protocols and primary studies, we charted the population, intervention, comparator, outcome, period, and study design to facilitate their evaluation and inclusion in future evidence syntheses. Results: We included 69 completed SRs; 37 on menu labeling, 9 on portion size control, and 23 on both. The types of menu labeling interventions studied were quantitative nutrient information (74%), interpretational guidance (48%), or contextual guidance (13%). Most reviews were from the United States, United Kingdom, and Canada. Most SRs included studies in establishments like cafeterias (51%) or restaurants (39%) and measured change in the quantity of food offered/ordered/consumed (96%). Phase 2 search yielded 24 primary studies; 16 experimental, 6 quasi-experimental, and 2 observational studies. Conclusion: The BoE on the effectiveness of menu labeling and portion size control is predominantly from the developed world, on nutrient information labeling and reporting impact on consumer food choice. There is a need for studies in the online environment and reporting distal health outcomes.
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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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| 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".