The Effectiveness of “Emolabeling” to Promote Healthy Food Choices in Children Preschool Through 5th Grade
Bibliographic record
Abstract
Obesity has become a growing global concern. Evidence indicates that ecological factors are most predictive of obesity among children, and that a new strategy, referred to as emolabeling, may effectively address ecological factors, although the extent to which it can influence food choice is not yet known, but tested here. Specifically, we tested the hypothesis that children aged 3 to 11 years will use emolabels, or emotional correlates of health (i.e. healthy-happy, unhealthy-sad), to make healthy food choices. A cross-sectional design was used with two phases. In Phase 1, children were taught how to use emolabels with a “faces of health” lesson. In Phase 2, children made choices between containers that were laid out on a large table in pairs and varied by taste (tastes good, no information), social norms (popular, not popular), branding (image of a minion, no image), or preference (told what food was in each container). A control pair was labeled with only emoticons. The order and presentation of the containers were counterbalanced for each variation. Results showed that a significant proportion of children in the pre-literacy and the early literacy grades used emoticons to specifically make healthy food choices in each variation (p < .05 for all tests), except when children were told what foods were in the containers. In all, emolabeling effectively influenced food choices for healthy foods among children aged 3 to 11 years, more so than labeling for taste, social norms, and branding, but not preference.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".