Does nutrition information on menus impact food choice? Comparisons across two hospital cafeterias
Bibliographic record
Abstract
OBJECTIVE: Food prepared and consumed away from home accounts for a significant proportion of dietary intake among Canadians. Currently, Canadians receive little or no nutrition information when eating in restaurant and fast-food outlets. The present study examined the impact of nutrition information on menus in hospital cafeterias on noticing and perceived influence of nutrition information and on food consumption. DESIGN: Cross-sectional surveys. SETTING: Exit surveys (n 1003) were conducted in two hospital cafeterias. The 'intervention' site featured energy (calorie), sodium and fat content on digital menu boards, as well as a health logo for 'healthier' items. The intervention site had also revised its menu items to improve the nutrient profiles. The 'control' site provided limited nutrition information at the point of sale. SUBJECTS: Cafeteria patrons recruited using the intercept technique. RESULTS: Significantly more respondents at the intervention site reported noticing nutrition information (OR = 7·6, P < 0·001) and using nutrition information to select their food items (OR = 3·3, P < 0·001) compared with patrons at the control site, after adjusting for sociodemographic factors. Patrons at the intervention site consumed significantly less energy (-21 %, P < 0·001), sodium (-23 %, P < 0·001), saturated fat (-33 %, P < 0·001) and total fat (-37 %, P < 0·001) than patrons at the control site. CONCLUSIONS: A nutritional programme, including nutrition information on menus and improved nutrition profile of food offerings, was associated with substantial reductions in energy, sodium and fat consumption. The results are consistent with a positive impact of menu labelling.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".