The influence of menu labelling on food choices among children and adolescents: a systematic review of the literature
Bibliographic record
Abstract
INTRODUCTION: Childhood obesity is a serious public health concern internationally, and population-level interventions are needed to support healthy food choices. Existing reviews of menu labelling have focused predominantly on adults. However, childhood and adolescence are distinct periods of development during which longer term eating behaviours and food preferences are established. Although some studies have examined the effect of menu labelling among children and adolescents, no reviews have synthesised this evidence. OBJECTIVE: To assess whether menu labelling influences the amount of calories ordered by children and adolescents (or parents on behalf of youth) in food outlets including restaurants and cafeterias. METHODS: Comprehensive literature searches were conducted in Medline, Scopus, PsycINFO, CINAHL, SocINDEX and Embase databases. Eleven relevant studies were identified from an initial search yielding 1,682 results. Studies were assessed using a validated quality assessment tool. RESULTS: Examinations of hypothetical food purchases in artificial environments suggest that menu labelling may be efficacious in reducing calories purchased for or by children and adolescents. Real-world studies are less supportive, although school-based studies were generally positive. It is unclear whether contextual or interpretive menu-labelling formats are more effective compared to numeric calorie information alone. CONCLUSION: Evidence supporting the impact of menu labelling on lowering the energy content of restaurant and cafeteria food choices made for or by children or adolescents is limited. There remains a need for high-quality studies conducted in real-world settings.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".